MobileTelephone Use Is Associated With
Changesin Cognitive Functionin
Michael J. Abramson,1,2* Geza P. Benke,1,2Christina Dimitriadis,1,2Imo O. Inyang,1,2Malcolm R. Sim,1,2
Rory S.Wolfe,1and Rodney J. Croft2,3,4
PreventiveMedicine,MonashUniversity, Melbourne,Victoria, Australia
As part of the Mobile Radiofrequency Phone Exposed Users’ Study (MoRPhEUS), a cross-sectional
epidemiological study examined cognitive function in secondary school students. We recruited 317,
7th grade students (144 boys, 173 girls, median age 13 years) from 20 schools around Melbourne,
Australia. Participants completed an exposure questionnaire based on the Interphone study, a
computerised cognitive test battery, and the Stroop colour-word test. The principal exposure metric
was the total number of reported mobile phone voice calls per week. Linear regression models were
fitted to cognitive test response times and accuracies. Age, gender, ethnicity, socio-economic status
and handedness were fitted as covariates and standard errors were adjusted for clustering by school.
The accuracy of working memory was poorer, reaction time for a simple learning task shorter,
associative learning response time shorter and accuracy poorer in children reporting more mobile
phone voice calls. There were no significant relationships between exposure and signal detection,
movement monitoring or estimation. The completion time for Stroop word naming tasks was longer
for those reporting more mobile phone voice calls. The findings were similar for total short message
service (SMS, also known as text) messages per week, suggesting these cognitive changes were
unlikely due to radiofrequency (RF) exposure. Overall, mobile phone use was associated with faster
andless accurate responding tohigher level cognitive tasks.Thesebehavioursmay havebeen learned
through frequent use of a mobile phone. Bioelectromagnetics 30:678–686, 2009.
? 2009 Wiley-Liss, Inc.
Key words: mobile telephones; cognitive function; children
Mobile (cellular) telephones have become an
extremely popular form of electronic communication.
Australia has been a world leader in the uptake of this
the population) now using a mobile phone. There is
increasing use by children, with 23% of those between
increases with age and girls are somewhat more likely
to own a mobile phone than boys [Downie and
However, this widespread exposure to radio-
frequency (RF) fields has been accompanied by
increasing concern about potential adverse health
effects. A number of expert reviews of scientific
Additional Supporting Information may be found in the online
version of this article.
Grant sponsor: The Australian Centre for Radiofrequency
Bioeffects Research and Geza Benke are supported by the
National Health Medical Research Council of Australia.
*Correspondence to: Michael J. Abramson, School of Public
Health & Preventive Medicine, Monash University, The Alfred,
Melbourne, Victoria 3004, Australia.
Received for review 10 September 2008; Final revision received
22 April 2009
Published online 30 July 2009 in Wiley InterScience
literature and reports from national authorities have
been published [Krewski et al., 2004; Stewart et al.,
2004; Health Council of the Netherlands 2007;
Report From SSI’s Independent Expert Group on
Electromagnetic Fields, 2008]. While the possibility
of tumours was the initial concern about RF exposure
from mobile phone use, recently there has been
increasing concern about other effects on the brain.
This is becoming a focus of research because there is
little information in published literature, particularly in
relation to children whose brains are still developing.
mobile phone exposure1does alter brain activity in
young adults [Croft et al., 2008], particularly alpha
(the 8–13Hz) electroencephalographic index of neural
for children, because it has been argued that different
head shapes and thinner skulls may make them more
susceptible to RF energy [Kheifets et al., 2005; Wiart
et al., 2005; Conil et al., 2008]. RF exposure from a
mobile phonehandsethasbeenassociatedwith slightly
shorter reaction times in children [Preece et al., 2005].
However, other short-term experimental exposures
have not found any consistent effects on cognitive
function in children [Haarala et al., 2005]. Recently,
a follow-up of a national birth cohort has suggested
that behavioural difficulties around the age of school
entry were more likely among children who had been
et al., 2008].
To address this lack of evidence regarding
Users’ Study (MoRPhEUS). The aims were: to assess
exposure to mobile telephones in secondary school
students, and to determine whether there were any
associations between this exposure and cognitive
A cross-sectional clustered study was conducted
during 2005 and 2006. We recruited 13 government,
4 Catholic and 3 independent secondary schools from
around Melbourne, Australia. The numbers of schools
chosen represent the proportions of secondary students
attending each sector in Victoria. At each school, one
7th grade home-room class (typical age, 12–13 years)
was randomly selected to participate. Parents or guard-
ians of children in the selected class were sent
information packages explaining the study. To be
included, students had to attend grade 7 at a participat-
able to understand the information on the plain English
with study requirements. We excluded students with
a known cognitive disorder and those receiving
medication or other drugs, known to impair or alter
Of the 479 students invited, 317 (66%) partici-
pated in the study—195 from government, 65 from
Catholic and 57 from independent schools. We
recruited 145 (46%) boys and 172 (54%) girls. The
median age was 13 (range 11–14) years. The vast
majority (266 or 84%) were born in Australia, and
81(26%) spoke a language other than English at home.
Questionnaires were completed by participating chil-
dren and their parents. Exposure to mobile telephones
was assessed with a modified version of the Interphone
questionnaire [Cardis et al., 2007]. The relevant
questions are given in the Appendix.
MoRPhEUS was approved by the Standing
Committee on Ethics in Research Involving Humans
at Monash University, the Department of Education
and Training, the Catholic Education Office and the
principals of all participating schools. Children and
their parents/guardians gave informed written consent.
Cognitive function was assessed with a com-
puterised psychometric test battery CogHealthTM
(CogState, Melbourne, Australia, 2005) and the Stroop
colour-word test. Research staff were trained in
administering tests and performing measurements.
that tested the following cognitive function domains:
. Signal detection: Simple reaction time measured
time taken to detect the presence of and perform
a motor response to a stimulus, while choice
reaction time had the added cognitive demand of
discriminating between two stimuli. Accuracy was
employed to index possible biased responding.
These tasks tested very basic information processing
1‘Exposure’ is used to denote different circumstances in different
disciplines. In bioelectromagnetics literature it is typically used to
denote actual exposure to the electromagnetic fields themselves.
However, in the present article as in most of the epidemiological
literature, ‘exposure’ is used to mean reported mobile phone use,
which relates to actual electromagnetic fields to differing degrees,
depending on type and use of phone, as well as the accuracy of
subjective reports of such mobile phone use.
and were used to assess a variety of possible impacts
on neural function, including acute mobile phone
exposure [Preece et al., 1999].
. Working memory: The one-back task indexed the
ability to flexibly hold information in short-term
memory. It required the child to keep an item in
memory for a short period so that it could be
compared to another item, and then the first item
was discarded from memory and the latter one
stored in its place. The two-back task required
keeping an item in memory to compare with another
item, following an intervening item which was then
discarded. The tasks were assessed in terms of
reaction time and accuracy, and required strong
mnemonic and attentional processes. There are
currently mixed results about possible acute effects
of mobile phone exposure on working memory
[Koivisto et al., 2000; Krause et al., 2000].
. Simple (One card) learning was a continuous visual
recognition task that assessed visual recognition
memory and attention. Children were presented with
an image of a playing card and had to respond ‘Yes’
or ‘No’, depending on whether the card had
previously been displayed.
. Associative learning: Children were tested on
whether they were able to learn associations
between images of playing cards, unlike the simple
learning task which required children to remember
the cards themselves. Associative learning relates to
a separate cognitive domain compared to the tasks
described above and has been employed to assess
cognitive impairment due to illicit recreational drug
use [Croft et al., 2001].
. Movement monitoring/estimation (moving card
monitoring task) indexed the ability to track and
predict the motion of an object. Unlike the cognitive
abilities indexed above, this was more akin to
performance of ball sports, such as football. Move-
ment monitoring/estimation has proven particularly
useful in assessing psychomotor performance,
similar to that associated with drug-related driving
impairment [Silber et al., 2006].
Students wore headphones to present auditory
cues, were given a trial run of all tasks to standardise
learning effects, and completed all tasks in succession
while the computer program recorded their response
times and accuracies.
The Stroop colour-word test involved reading
words representing the names of colours [Stroop,
1935]. The first subtask (form A) required the child to
read 50 words printed in black ink. The child was also
asked to name each of four colours presented in
50 meaningless symbols (form C). The interference
subtasks (forms B and D) required the child to read the
word or identify the colour in which it was printed,
respectively, where the word and colour were incon-
gruous. Completion times and errors were recorded for
each form. There is extensive literature on this test,
[Croft et al., 2001].
The primary exposure metric presented in this
analysis was log10total reported number of voice calls
made and received per week (LogTotalCalls), which
was normally distributed. When the respondent speci-
fied a range of calls per week, the arithmetic average
was chosen. An offset of 1 was added to include valid
zeroes and the distribution truncated at 70 to exclude
extreme outliers. Secondary exposure metrics were
log10total number of short message service (SMS, also
known as text) messages made and received per week
(LogTotalSMS) and duration (years of mobile phone
to the distribution of total SMS prior to log trans-
In accordance with the manufacturer’s recom-
mendations, mean response time for true positives and
true negatives was log10transformed and accuracy was
analysed by comparing form B with form A, and form
D with form C. Separate multiple linear regression
models were fitted to each of these outcomes with
LogTotalCalls, LogTotalSMS or duration as the
predictor. Standard errors were adjusted for clustering
College Station, TX, 2007).
Age, gender, ethnicity (languages other than
English spoken at home), socio-economic status and
handedness were fitted as covariates. Socio-economic
status was estimated from the Socio-Economic Index
for Areas (SEIFA) of advantage/disadvantage for the
postcode of residence [Australian Bureau of Statistics,
2006], (www.abs.gov.au/AUSSTATS/abs@nsf). Anal-
yses were conducted in SPSS, version 15.0 (SPSS,
Chicago, IL, 2006) and Stata version 10 [StataCorp,
Description of Exposure and Cognitive
Most (299 or 94%) of the 317 children had used
a mobile phone and 243 (77%) had their own phones.
680Abramson et al.
The median total number of reported voice calls per
15 calls (Fig. 1). The median number of reported SMS
messages was also 8 (IQR 2.5–20) per week (Fig. 1).
The Kendall nonparametric correlation between the
reported numbers of weekly voice calls and SMS was
0.4 (P<0.001). The median reported duration of
mobile phone use was 1.74 (IQR 0.89–2.63) years
(Fig. 1). The CogHealthTMbattery was completed by
few data missing for some tests after removal of
nonvalid zeroes; thus some analyses present slightly
smaller numbers. Descriptive statistics for the untrans-
for the Stroop colour-word test in Table 2.
Associations Between Total Voice Calls and
Results of the regression models fitted to each
CogHealthTMoutcome are summarised in Table 3.
receiving more voice calls per week were associated
with shorter response times or less accurate responses.
Students who reported more voice calls per week
demonstrated shorter response times for the simple and
associative learning tasks. Those who reported more
voice calls also performed less accurately on working
reported mobile phone use.The box representsthe interquartile range (IQR) and the verticalline
the median value.Outliers (cases with values1.5^3 times the IQR above the 75th percentile) are
represented by ‘o’and extreme observations (cases with values more than three times the IQR
TABLE 1. Descriptive Statistics for Cognitive Tests in the CogHealthTMBattery
Test ParameterN MedianIQRa
Simple reaction time Response timeb
Choice reaction time
One card learning task
Associative learning task
aInterquartile range (25th percentile–75th percentile).
bResponse time in ms for true positives and true negatives.
cHit rate (%).
MobileTelephoneUsein Children 681
memory tests (one-back and two-back tasks) and the
associative learning task. Signal detection (simple and
choice reaction times) and movement monitoring/
estimation were not related to the total number of
voice calls per week. There was some evidence that
associations with accuracy of working memory
(P¼0.04 for interaction in both one-back and two-
back tasks) were stronger for boys than girls, but there
was no effect modification for ethnicity.
The regression models fitted to the Stroop com-
pletion time ratios are summarised in Table 4. Students
who reported making and receiving more voice calls
per week took significantly longer to complete form B
(compared to form A), indicating that interference by
the word printed in an incongruous colour was greater
among more frequent mobile phone users.
Associations Between SMS Messages and
Table 5 presents relationships between total SMS
messages per week and cognitive outcomes. Students
who reported making and receiving more SMS
messages demonstrated shorter response times to the
simple learning task, but less accurate responses to the
working memory (one-back and two-back) and asso-
ciative learning tasks. Signal detection and movement
monitoring/estimation were not related to total SMS
messages. There were also no significant relationships
between total SMS messages and Stroop completion
time ratios (Table 4). There was minimal evidence of
effect modification by gender or ethnicity.
To investigate possible joint cognitive effects of
the numberof voice calls and SMS messages per week,
the students were divided into four groups: low calls
tertile) SMS (n¼40), ‘high’ (upper tertile) calls and
low SMS (n¼36), and ‘high’ calls and ‘high’ SMS use
(n¼60). The effects on working memory were
confined to the group with high use of both modalities.
The accuracy of the simple reaction test was also
reduced in this group. On the other hand, the effect on
simple learning was apparent in all groups with more
TABLE 2. Descriptive Statistics for the Stroop Colour-Word Test (n¼317)
FormParameter MedianInterquartile range
A Time (s)
TABLE 3. Associations (Regression Coefficients and 95% Confidence Intervals) Between Total Reported Voice Calls Per Week
and Cognitive Outcomes Adjusted for Age, Gender, Ethnicity, Socio-Economic Status, Handedness and Clustering by School
Simple reaction timeResponse timea
Choice reaction time
One card learning task
Associative learning task
aLog10(response time in ms for true positives and true negatives).
bArcsine transformed hit rate.
Bold values indicates P<0.05.
682Abramson et al.
calls (Table S1, see Online Supplementary Data).
There were no significant relationships between
duration of mobile phone use and any of the cognitive
outcomes (data not shown).
MoRPhEUS is the first cross-sectional epidemio-
logical study to investigate cognitive biological effects
of mobile phone exposure in school-aged children. By
the age of 13, most of our sample had used a mobile
phone, which is comparable to Swedish data [Soderqv-
ist et al., 2007]. We found that students who reported
and in particular more of both, demonstrated shorter
response times on learning tasks, but less accurate
working memory. Consistent with this, those who
also exhibited poorer inhibitory function (as indexed
by poorer performance on the Stroop). The findings
were quite robust when different statistical methods
(unadjusted nonparametric, multivariate analysis of
covariance) were employed.
The association between mobile phone use and
cognition was not restricted to a particular task or
cognitive ability. Rather greater mobile phone use was
related to poorer accuracy on working memory and
associative learning tasks, and faster reaction times
on the simple and associative learning tasks. This
suggests that rather than being related to a particular
cognitive function, it may be related to an impulsive
response style of children. In this context, impulsive
response style or ‘impulsive behaviour’ refers to
the tendency of children to respond before they know
used mobile phones more were faster but less accurate
onanumberof tasks,suggestingthattheymaybe more
impulsive than other children, favouring a quick, and
not accurate, solution. It is noteworthy that impulsive
behaviour is associated with the type of behavioural
TABLE 4. Associations (Regression Coefficients and 95% Confidence Intervals) Between
Either Total Reported Voice Calls or SMS Messages Per Week and Stroop Time Ratios,
Adjusted for Age, Gender, Ethnicity, Socio-Economic Status, Handedness and Clustering by
Bold values indicates P<0.05.
TABLE 5. Associations (Regression Coefficients and 95% Confidence Intervals) Between Total Reported SMS Messages
Per Week and Cognitive Outcomes Adjusted for Age, Gender, Ethnicity, Socio-Economic Status, Handedness and Clustering by
Test ParameterCoefficient95% CIP-value
Simple reaction time Response timea
Choice reaction time
One card learning task
Associative learning task
aLog10(response time in ms for true positives and true negatives).
bArcsine transformed hit rate.
Bold values indicates P<0.05.
problems in children reported to be associated with
prenatal and postnatal mobile phone exposure [Divan
et al., 2008].
Another possible explanation that should be
considered is whether the computer key pressing
behaviour itself may have been altered, rather than the
cognitive processes preceding these behaviours. How-
ever, we consider this explanation unlikely because
speed and accuracy of key pressing were differentially
were found with only some tasks, suggesting that the
cognitions and not the key pressing behaviour were
determining the relationships.
not possible to determine whether RF exposure had
affected cognitive function or whether these findings
represented pre-existing impulsive behaviours and/or
learned behaviours from more frequent use of mobile
phones. The latter possibility is suggested by functions
texting’, that in effect, train the user to favour speed
over accuracy. Similar results were also found for SMS
messages where there is very little RF exposure.
Although SMS employs similar techniques to transmit
the handset is typically held away from the head when
in use. Because the exposure data were collected at
the same time as cognitive testing, reverse causality
mobile phone use. Because the data are inconsistent
with these effects resulting from RF exposure, specu-
lation about possible mechanisms of biological inter-
actions with children’s brains is not warranted.
There have been very few previous studies of
mobile phone biological effects in children, and differ-
ences in methodology make it difficult to directly
compare our findings. A feasibility study (MobilEe)
involving 69 children, 70 adolescents and their parents,
in personal dosimetry, interview, quality of life
questionnaire and symptom diary was conducted in
Munich [Radon et al., 2004]. The investigators
concluded that 24h monitoring was feasible and the
questionnaires were well accepted by participants.
However, they have not yet reported whether there
was any association between RF exposure and quality
of life or other outcomes.
Recently, a large Danish national birth cohort
followed children up to the age of 7 years [Divan et al.,
2008]. Behavioural problems in the children, such as
emotional, hyperactivity, conduct and peer problems
were assessed. Both prenatal and postnatal maternal
exposures to mobile phones were associated with a
1.8-fold increase in reported behavioural problems.
The analysis was adjusted for confounders such as sex
of child, age of mother, smoking during pregnancy,
mother’s psychiatric problems and socio-occupational
levels. Although data on lifestyle factors, diet and
environmental exposures were collected prospectively,
exposure to a mobile phone during pregnancy was only
determined at age 7. While this exposure assessment
could have been affected by recall bias, the mothers
were not aware of the hypothesis being tested.
There is limited consistency in the few studies of
experimental RF exposures in children. A randomised
cross-over trial with a 902MHz mobile phone handset
was undertaken with 18 children, aged 10–12 years,
using a cognitive assessment system [Preece et al.,
2005]. Simple reaction time was reduced by 17ms
during the 25Wexposure condition compared to sham
exposure (P¼0.02). However, the authors dismissed
this finding after adjustment for multiple comparisons.
Another trial involved 32 children, aged 10–14 years,
who completed a battery of cognitive tests during
exposuretoactiveand inactivemobile phones[Haarala
et al., 2005]. There were no consistent effects of RF
exposure on reaction time, choice reaction time,
vigilance or n-back tasks. While many of these tasks
were similar to those that we employed, the mean
duration of experimental exposure was only 50min.
Nonetheless, some cognitiveeffects of short-term
experimental exposures to RF energy similar to that
emitted by mobile phones have been previously
reported, mainly in small samples of healthy young
adults. A meta-analysis of data from 10 such blinded
of RF exposure on attention and working memory. In
particular, reaction times to the subtraction task were
shorter and two-back response times were longer, but
accuracy was poorer under conditions of RF exposure.
Although we did not employ a subtraction task, the
poorer accuracy to the two-back task is very similar to
what we found in those children who reported more
voice calls per week.
The strengths of MoRPhEUS include cluster
sampling of all three school sectors, a good partic-
a well-validated and age-appropriate test battery.
CogHealthTMhas been previously used to document
cognitive impairment due to sleep deprivation or
alcohol intake in young adults [Falleti et al., 2003]
among normal children between 8 and 12 years of age
[Mollica et al., 2005]. The Stroop test has also been
successfully used in children of this age [Shum et al.,
was acceptable, but unfortunately we have no data on
684Abramson et al.
nonparticipants to assess representativeness. The anal-
ysis was adjusted for clustering by school, although the
within-school correlations were quite weak.
The limitations include reliance on self-reported
exposure to mobile phones. However, the Interphone
modified phones [Vrijheid et al., 2006] and network
billing records in adults [Vrijheid et al., 2008]. This
latter approach cannot be adopted in children who
mainly use prepaid mobile phone services. Data on
duration of calls were not presented because these have
been previously shown to be less accurately reported
than number of calls [Samkange-Zeeb et al., 2004]. We
accept that self-reported use of mobile phones is a
poor surrogate for RF exposure and have discussed
alternative approaches for epidemiological studies
elsewhere [Inyang et al., 2008].
Future epidemiological research needs to adopt a
cohort design to determine the correct temporal
sequence of mobile phone use and cognitive effects.
We are collecting longitudinal data in MoRPhEUS to
better investigate changes in exposure and cognitive
function over time. Studies also need to be conducted
on younger children as exposure is becoming more
common in younger age groups. There is reported
exposure to mobile phones, albeit infrequently, in 30%
of 6- to 13-year-olds in Australia [Downie and
Glazebrook, 2007]. Although the cognitive effects
found might appear subtle, the almost ubiquitous
exposure means that if caused by mobile phones,
they could possibly have considerable public health
We thank David Darby for advice on Cog-
HealthTMand Dean McKenzie foradvice on the Stroop
test. Fieldwork was performed by Mahendra Arnold,
Patricia Berry, Jill Blackman, Miranda Davies, Emmy
Gavrilidis, Suzy Giuliano, Steve Haas, Richard Lunz,
Juliet Muthieu, Shyamala Nataraj, Andrea Neale,
Haydn Ryan and Margaret Stebbing. We thank all
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Selected exposure questions from the modified
B3aHave you ever used mobile phones to make or
Do you currently own a mobile phone?
Do you currently use a mobile phone?
How old were you when you first started to use
a mobile phone?
What is the average number of calls you make
per week? You can give me a range if that is
What is the average number of calls you receive
on your mobile phone per week? You can give
me a range if that is easier.
What is the average number of text (SMS)
messages you send and receive per week? You
can give me a range if that is easier.
686Abramson et al.