Mobile telephone use is associated with changes in cognitive function in young adolescents

Article (PDF Available)inBioelectromagnetics 30(8):678-86 · December 2009with145 Reads
DOI: 10.1002/bem.20534 · Source: PubMed
Abstract
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 and less accurate responding to higher level cognitive tasks. These behaviours may have been learned through frequent use of a mobile phone.
Bioelectromagnetics 30:678^686 (2009)
MobileTelephone Use Is Associated With
Changes in Cognitive Function in
Young Adolescents
Michael J. Abramson,
1,2
*GezaP.Benke,
1,2
Christina Dimitriadis,
1,2
Imo O. Inyang,
1,2
Malcolm R. Sim,
1,2
Rory S. Wolfe,
1
and Rodney J. Croft
2,3,4
1
Department of Epidemiology & Preventive Medicine, School of Public Health &
Preventive Medicine, Monash University, Melbourne,Victoria, Australia
2
Australian Centre for Radiofrequency Bioeffects Research, Australia
3
Brain Sciences Institute, Swinburne University of Technology, Hawthorn,
Victoria, Australia
4
Department of Psychology, University of Wollongong,Wollongong,
New South Wales, 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
and less accurate responding to higher level cognitive tasks. These behaviours may have been learned
through frequent use of a mobile phone. Bioelectromagnetics 30:678686, 2009.
2009 Wiley-Liss, Inc.
Key words: mobile telephones; cognitive function; children
INTRODUCTION
Mobile (cellular) telephones have become an
extremely popular form of electronic communication.
Australia has been a world leader in the uptake of this
technology with 20 million subscribers (or up to 94% of
the population) now using a mobile phone. There is
increasing use by children, with 23% of those between
the ages of 6 and 13 owning a mobile phone. Ownership
increases with age and girls are somewhat more likely
to own a mobile phone than boys [Downie and
Glazebrook, 2007].
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
2009 Wiley-Liss,Inc.
—————
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.
E-mail: michael.abramson@med.monash.edu.au
Received for review 10 September 2008; Final revision received
22 April 2009
DOI 10.1002/bem.20534
Published online 30 July 2009 in Wiley InterScience
(www.interscience.wiley.com).
literature and reports from national authorities have
been published [Krewski et al., 2004; Stewart et al.,
2004; Health Council of the Netherlands 2007;
Repacholi et al., 2007; Barnes et al., 2008; Fifth Annual
Report From SSIs 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.
There is now sufcient experimental evidence that
mobile phone exposure
1
does alter brain activity in
young adults [Croft et al., 2008], particularly alpha
(the 813 Hz) electroencephalographic index of neural
activity. Such biological effects may be more signicant
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 phone handset has been associated with 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 difculties around the age of school
entry were more likely among children who had been
exposed to mobile phones in utero or postnatally [Divan
et al., 2008].
To address this lack of evidence regarding
cognitive biological effects among children, the authors
established the Mobile Radiofrequency Phone Exposed
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
function.
METHODS
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, 1213 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-
ing school and the student and parent/guardian had to be
able to understand the information on the plain English
language sheet and consent form, and willing to comply
with study requirements. We excluded students with
a known cognitive disorder and those receiving
medication or other drugs, known to impair or alter
cognitive function.
Of the 479 students invited, 317 (66%) partici-
pated in the study195 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 1114) 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 modied version of the Interphone
questionnaire [Cardis et al., 2007]. The relevant
questions are given in the Appendix.
Clearances
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 Ofce and the
principals of all participating schools. Children and
their parents/guardians gave informed written consent.
Outcome Assessment
Cognitive function was assessed with a com-
puterised psychometric test battery CogHealth
TM
(CogState, Melbourne, Australia, 2005) and the Stroop
colour-word test. Research staff were trained in
administering tests and performing measurements.
The battery included several well-validated instruments
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 elds 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 elds to differing degrees,
depending on type and use of phone, as well as the accuracy of
subjective reports of such mobile phone use.
MobileTelephone Use in Children 679
Bioelectromagnetics
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 exibly 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 rst 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 rst 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,
partially because of its strong face validity and its use as
a measure of illicit drug use effects on focused attention
[Croft et al., 2001].
Statistical Analysis
The primary exposure metric presented in this
analysis was log
10
total reported number of voice calls
made and received per week (LogTotalCalls), which
was normally distributed. When the respondent speci-
ed 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
log
10
total number of short message service (SMS, also
known as text) messages made and received per week
(LogTotalSMS) and duration (years of mobile phone
use). Again, the same offset and truncation were applied
to the distribution of total SMS prior to log trans-
formation.
In accordance with the manufacturers recom-
mendations, mean response time for true positives and
true negatives was log
10
transformed and accuracy was
expressed as arcsine transformed hit-rate for each test in
the CogHealth battery. Stroop times and error rates were
analysed by comparing form B with form A, and form
D with form C. Separate multiple linear regression
models were tted to each of these outcomes with
LogTotalCalls, LogTotalSMS or duration as the
predictor. Standard errors were adjusted for clustering
by school using a robust variance estimator (StataCorp.,
College Station, TX, 2007).
Age, gender, ethnicity (languages other than
English spoken at home), socio-economic status and
handedness were tted 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,
2007].
RESULTS
Description of Exposure and Cognitive
Test Results
Most (299 or 94%) of the 317 children had used
a mobile phone and 243 (77%) had their own phones.
680 Abramson et al.
Bioelectromagnetics
The median total number of reported voice calls per
week was 8, with an interquartile range (IQR) from 4 to
15 calls (Fig. 1). The median number of reported SMS
messages was also 8 (IQR 2.520) 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 0.892.63) years
(Fig. 1). The CogHealth
TM
battery was completed by
315 students and the Stroop test by all 317. There were a
few data missing for some tests after removal of
nonvalid zeroes; thus some analyses present slightly
smaller numbers. Descriptive statistics for the untrans-
formed response times and accuracies of cognitive tests
are given in Table 1, and response times and error counts
for the Stroop colour-word test in Table 2.
Associations Between Total Voice Calls and
Cognitive Outcomes
Results of the regression models tted to each
CogHealth
TM
outcome are summarised in Table 3.
Negative regression coefcients meant that making and
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
Fig. 1. Boxplots oftotalreported voice calls per week, totalreported SMS calls per week and years
reported mobile phone use. The box represents the interquartile range (IQR) and the vertical line
the median value. Outliers (cases with values 1.5 ^3 times the IQR above the 75th percentile) are
represented by o and extreme observations (cases with values more than three times the IQR
above the 75th percentile) by *’.
TABLE 1. Descriptive Statistics for Cognitive Tests in the CogHealth
TM
Battery
Test Parameter N Median IQR
a
Simple reaction time Response time
b
315 318 277387
Accuracy
c
315 97.2 92.1100
Choice reaction time Response time
b
314 568 493679
Accuracy
c
314 90.9 85.796.8
One-back task Response time
b
313 743 636869
Accuracy
c
313 88.2 76.993.8
Two-back task Response time
b
312 834 700986
Accuracy
c
312 75.7 57.388.2
One card learning task Response time
b
311 895 7351092
Accuracy
c
311 54.8 45.266.7
Associative learning task Response time
b
314 1276 10911495
Accuracy
c
314 71.2 60.478.8
Movement monitoring/estimation Response time
b
313 473 407555
Accuracy
c
313 83.3 76.990.9
a
Interquartile range (25th percentile75th percentile).
b
Response time in ms for true positives and true negatives.
c
Hit rate (%).
MobileTelephone Use in Children 681
Bioelectromagnetics
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
(P ¼ 0.04 for interaction in both one-back and two-
back tasks) were stronger for boys than girls, but there
was no effect modication for ethnicity.
The regression models tted to the Stroop com-
pletion time ratios are summarised in Table 4. Students
who reported making and receiving more voice calls
per week took signicantly 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
Cognitive Outcomes
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 signicant relationships
between total SMS messages and Stroop completion
time ratios (Table 4). There was minimal evidence of
effect modication by gender or ethnicity.
To investigate possible joint cognitive effects of
the number of voice calls and SMS messages per week,
the students were divided into four groups: low calls
and low SMS use (n ¼ 168), low calls and high (upper
tertile) SMS (n ¼ 40), high (upper tertile) calls and
low SMS (n ¼ 36), and high calls and high SMS use
(n ¼ 60). The effects on working memory were
conned 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 (n ¼ 317)
Form Parameter Median Interquartile range
A Time (s) 20.9 18.623.1
Errors 0 00
B Time (s) 22.7 20.326.2
Errors 0 00
C Time (s) 28.4 25.631.6
Errors 0 01
D Time (s) 46.4 41.253.7
Errors 1 02
(BA)/A Time ratio 0.10 0.030.19
(DC)/C Time ratio 0.64 0.490.82
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
Test Parameter Coefcient 95% CI P-value
Simple reaction time Response time
a
0.005 0.021, 0.031 0.70
Accuracy
b
0.050 0.104, 0.004 0.07
Choice reaction time Response time
a
0.004 0.017, 0.024 0.71
Accuracy
b
0.030 0.093, 0.033 0.34
One-back task Response time
a
0.014 0.009, 0.036 0.23
Accuracy
b
0.091 0.170, 0.013 0.03
Two-back task Response time
a
0.018 0.039, 0.002 0.08
Accuracy
b
0.098 0.169, 0.027 0.01
One card learning task Response time
a
0.051 0.083, 0.020 0.003
Accuracy
b
0.030 0.067, 0.007 0.11
Associative learning task Response time
a
0.028 0.049, 0.007 0.01
Accuracy
b
0.072 0.108, 0.037 <0.001
Movement monitoring/estimation Response time
a
0.001 0.016, 0.019 0.87
Accuracy
b
0.008 0.056, 0.041 0.75
a
Log
10
(response time in ms for true positives and true negatives).
b
Arcsine transformed hit rate.
Bold values indicates P < 0.05.
682 Abramson et al.
Bioelectromagnetics
than a low use of a mobile phone. There were effects on
associative learning in those with high numbers of voice
calls (Table S1, see Online Supplementary Data).
There were no signicant relationships between
duration of mobile phone use and any of the cognitive
outcomes (data not shown).
DISCUSSION
MoRPhEUS is the rst 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
making or receiving more voice or SMS calls per week,
and in particular more of both, demonstrated shorter
response times on learning tasks, but less accurate
working memory. Consistent with this, those who
reported making or receiving more voice calls per week
also exhibited poorer inhibitory function (as indexed
by poorer performance on the Stroop). The ndings
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
the correct answer. Corresponding to this, children who
used mobile phones more were faster but less accurate
on a number of tasks, suggesting that they may be 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 Coefcients and 95% Condence 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
School
Ratio Exposure Coefcient 95%CI P-value
(BA)/A LogTotalCalls 0.040 0.010, 0.070 0.01
LogTotalSMS 0.013 0.017, 0.042 0.38
(DC)/C LogTotalCalls 0.006 0.0554, 0.067 0.84
LogTotalSMS 0.014 0.063, 0.036 0.58
Bold values indicates P < 0.05.
TABLE 5. Associations (Regression Coefcients and 95% Condence Intervals) Between Total Reported SMS Messages
Per Week and Cognitive Outcomes Adjusted for Age, Gender, Ethnicity, Socio-Economic Status, Handedness and Clustering by
School
Test Parameter Coefcient 95% CI P-value
Simple reaction time Response time
a
0.003 0.013, 0.019 0.66
Accuracy
b
0.041 0.097, 0.014 0.14
Choice reaction time Response time
a
0.002 0.016, 0.021 0.80
Accuracy
b
0.048 0.100, 0.003 0.07
One-back task Response time
a
0.006 0.011, 0.022 0.46
Accuracy
b
0.073 0.136, 0.010 0.03
Two-back task Response time
a
0.017 0.035, 0.001 0.06
Accuracy
b
0.066 0.113, 0.020 0.008
One card learning task Response time
a
0.054 0.090, 0.019 0.005
Accuracy
b
0.032 0.074, 0.009 0.12
Associative learning task Response time
a
0.016 0.039, 0.008 0.17
Accuracy
b
0.049 0.079, 0.019 0.003
Movement monitoring/estimation Response time
a
0.006 0.006, 0.017 0.29
Accuracy
b
0.008 0.053, 0.037 0.72
a
Log
10
(response time in ms for true positives and true negatives).
b
Arcsine transformed hit rate.
Bold values indicates P < 0.05.
MobileTelephone Use in Children 683
Bioelectromagnetics
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
affected across the cognitive domains. The key pressing
behaviours were the same across tasks, but relationships
were found with only some tasks, suggesting that the
cognitions and not the key pressing behaviour were
determining the relationships.
Because this was a cross-sectional analysis, it was
not possible to determine whether RF exposure had
affected cognitive function or whether these ndings
represented pre-existing impulsive behaviours and/or
learned behaviours from more frequent use of mobile
phones. The latter possibility is suggested by functions
often used in conjunction with SMS, such as predictive
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
and receive information, it takes only a few seconds and
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
remains possible and that cognitive function could drive
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 childrens 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 difcult to directly
compare our ndings. 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 24 h 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,
mothers 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 902 MHz mobile phone handset
was undertaken with 18 children, aged 1012 years,
using a cognitive assessment system [Preece et al.,
2005]. Simple reaction time was reduced by 17 ms
during the 25 W exposure condition compared to sham
exposure (P ¼ 0.02). However, the authors dismissed
this nding after adjustment for multiple comparisons.
Another trial involved 32 children, aged 1014 years,
who completed a battery of cognitive tests during
exposure to active and inactive mobile 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 50 min.
Nonetheless, some cognitive effects 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
controlled trials has been published [Barth et al., 2008].
The reviewers found small but signicant pooled effects
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-
ipation rate and measurement of cognitive function with
a well-validated and age-appropriate test battery.
CogHealth
TM
has been previously used to document
cognitive impairment due to sleep deprivation or
alcohol intake in young adults [Falleti et al., 2003]
and to measure improvements in cognitive performance
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.,
2008]. The response from students invited to participate
was acceptable, but unfortunately we have no data on
684 Abramson et al.
Bioelectromagnetics
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
questionnaire has been validated against both software-
modied 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 7-year-olds in Denmark [Divan et al., 2008] and 23%
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
signicance.
ACKNOWLEDGMENTS
We thank David Darby for advice on Cog-
Health
TM
and Dean McKenzie for advice 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
participating schools, principals, teachers, parents and
students for their assistance.
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APPENDIX
Selected exposure questions from the modied
Interphone questionnaire:
B3a Have you ever used mobile phones to make or
receive calls?
B3b Do you currently own a mobile phone?
B4 Do you currently use a mobile phone?
B6 How old were you when you rst started to use
a mobile phone?
B7 What is the average number of calls you make
per week? You can give me a range if that is
easier.
B9 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.
B14 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.
686 Abramson et al.
Bioelectromagnetics
    • "However, this methodology has some limitations in that it may alter the real exposure as reported by Frei et al. [43] , due to shielding effects or potential variations in the normal behaviour of children when using the device. Besides our study, other epidemiological studies, such as the HERMES [30] and the ABCD [27, 44] cohort studies in Switzerland and the Netherlands respectively, the cross-sectional MoRPhEUs study in Australia [11] and the multicentre case control CEFALO study in Scandinavian countries and Switzerland [8] have characterised RF exposure. With the exception of ABCD cohort , the rest were created with the aim of assessing the exposure and effects of RF fields. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Analysis of the association between exposure to electromagnetic fields of non-ionising radiation (EMF-NIR) and health in children and adolescents is hindered by the limited availability of data, mainly due to the difficulties on the exposure assessment. This study protocol describes the methodologies used for characterising exposure of children to EMF-NIR in the INMA (INfancia y Medio Ambiente- Environment and Childhood) Project, a prospective cohort study. Methods/Design Indirect (proximity to emission sources, questionnaires on sources use and geospatial propagation models) and direct methods (spot and fixed longer-term measurements and personal measurements) were conducted in order to assess exposure levels of study participants aged between 7 and 18 years old. The methodology used varies depending on the frequency of the EMF-NIR and the environment (homes, schools and parks). Questionnaires assessed the use of sources contributing both to Extremely Low Frequency (ELF) and Radiofrequency (RF) exposure levels. Geospatial propagation models (NISMap) are implemented and validated for environmental outdoor sources of RFs using spot measurements. Spot and fixed longer-term ELF and RF measurements were done in the environments where children spend most of the time. Moreover, personal measurements were taken in order to assess individual exposure to RF. The exposure data are used to explore their relationships with proximity and/or use of EMF-NIR sources. Discussion Characterisation of the EMF-NIR exposure by this combination of methods is intended to overcome problems encountered in other research. The assessment of exposure of INMA cohort children and adolescents living in different regions of Spain to the full frequency range of EMF-NIR extends the characterisation of environmental exposures in this cohort. Together with other data obtained in the project, on socioeconomic and family characteristics and development of the children and adolescents, this will enable to evaluate the complex interaction between health outcomes in children and adolescents and the various environmental factors that surround them.
    Full-text · Article · Dec 2016
    • "For CP use, reaction time for the Stroop A interference task was significantly slower in girls among higher users compared to both low and non-users, with no association in boys. In our previous study [5] there was some evidence that associations between MP calls and accuracy of working memory were stronger in boys than girls. Given the inconsistencies between the MP and CP results, it is possible that the few significant differences which were observed may have been These are regression coefficients adjusted for age, gender, language other than English, handedness, and socioeconomic status. "
    [Show abstract] [Hide abstract] ABSTRACT: Use of mobile (MP) and cordless phones (CP) is common among young children, but whether the resulting radiofrequency exposure affects development of cognitive skills is not known. Small changes have been found in older children. This study focused on children’s exposures to MP and CP and cognitive development. The hypothesis was that children who used these phones would display differences in cognitive function compared to those who did not. We recruited 619 fourth-grade students (8-11 years) from 37 schools around Melbourne and Wollongong, Australia. Participants completed a short questionnaire, a computerised cognitive test battery, and the Stroop colour-word test. Parents completed exposure questionnaires on their child’s behalf. Analysis used multiple linear regression. The principal exposure-metrics were the total number of reported MP and CP calls weekly categorised into no use ('None'); use less than or equal to the median amount (‘Some’); and use more than the median (‘More’). The median number of calls/week was 2.5 for MP and 2.0 for CP. MP and CP use for calls was low; and only 5 of 78 comparisons of phone use with cognitive measures were statistically significant. The reaction time to the response-inhibition task was slower in those who used an MP ‘More’ compared to the ‘Some’ use group and non-users. For CP use, the response time to the Stroop interference task was slower in the ‘More’ group versus the ‘Some’ group, and accuracy was worse in visual recognition and episodic memory tasks and the identification task. In an additional exploratory analysis, there was some evidence of a gender effect on mean reaction times. The highest users for both phone types were girls. Overall, there was little evidence cognitive function was associated with CP and MP use in this age group. Although there was some evidence that effects of MP and CP use on cognition may differ by gender, this needs further exploration. CP results may be more reliable as parents estimated children’s phone use and the CPs were at home; results for CP use were broadly consistent with our earlier study of older children.
    Article · Dec 2016
    • "Also, results from experimental studies are not directly comparable with the results of observational studies as our study. Self-reported frequency of cell phone use in adolescents was associated with changes in some of the cognitive function test assessed one year later including working memory and learning, but not showing a clear direction of the association (Abramson et al., 2009; Thomas et al., 2010). A recent study carried out in children 8–11 years old found little evidence of a consistent association between cell phone or cordless phone use with specific cognitive functions including attentional function, working memory, and memory (Redmayne et al., 2016). "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Little is known about the exposure of young children to radiofrequency electromagnetic fields (RF-EMF) and potentially associated health effects. We assessed the relationship between residential RF-EMF exposure from mobile phone base stations, residential presence of indoor sources, personal cell phone and cordless phone use, and children's cognitive function at 5-6 years of age. Methods: Cross-sectional study on children aged 5-6 years from the Amsterdam Born Children and their Development (ABCD) study, the Netherlands (n=2354). Residential RF-EMF exposure from mobile phone base stations was estimated with a 3D geospatial radio wave propagation model. Residential presence of indoor sources (cordless phone base stations and Wi-Fi) and children's cell phone and cordless phone use was reported by the mother. Speed of information processing, inhibitory control, cognitive flexibility, and visuomotor coordination was assessed using the Amsterdam Neuropsychological Tasks. Results: Residential presence of RF-EMF indoor sources was associated with an improved speed of information processing. Higher residential RF-EMF exposure from mobile phone base stations and presence of indoor sources was associated with an improved inhibitory control and cognitive flexibility whereas we observed a reduced inhibitory control and cognitive flexibility with higher personal cordless phone use. Higher residential RF-EMF exposure from mobile phone base stations was associated with a reduced visuomotor coordination whereas we observed an improved visuomotor coordination with residential presence of RF-EMF indoor sources and higher personal cell phone use. Conclusions: We found inconsistent associations between different sources of RF-EMF exposure and cognitive function in children aged 5-6 years.
    Article · Jun 2016
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