STUDY PROTOCOL Open AccessA family based tailored counsell.docx

STUDY PROTOCOL Open Access
A family based tailored counselling to increase
non-exercise physical activity in adults with a
sedentary job and physical activity in their young
children: design and methods of a year-long
randomized controlled trial
Taija Finni1*, Arja Sääkslahti2, Arto Laukkanen1, Arto Pesola1
and Sarianna Sipilä3
Abstract
Background: Epidemiological evidence suggests that decrease in
sedentary behaviour is beneficial for health. This
family based randomized controlled trial examines whether
face-to-face delivered counselling is effective in
reducing sedentary time and improving health in adults and
increasing moderate-to-vigorous activities in children.
Methods: The families are randomized after balancing
socioeconomic and environmental factors in the Jyväskylä
region, Finland. Inclusion criteria are: healthy men and women
with children 3-8 years old, and having an
occupation where they self-reportedly sit more than 50% of
their work time and children in all-day day-care in
kindergarten or in the first grade in primary school. Exclusion
criteria are: body mass index > 35 kg/m2, self-
reported chronic, long-term diseases, families with pregnant
mother at baseline and children with disorders
delaying motor development.
From both adults and children accelerometer data is collected
five times a year in one week periods. In addition,

fasting blood samples for whole blood count and serum
metabonomics, and diurnal heart rate variability for 3
days are assessed at baseline, 3, 6, 9, and 12 months follow-up
from adults. Quadriceps and hamstring muscle
activities providing detailed information on muscle inactivity
will be used to realize the maximum potential effect
of the intervention. Fundamental motor skills from children and
body composition from adults will be measured at
baseline, and at 6 and 12 months follow-up. Questionnaires of
family-influence-model, health and physical activity,
and dietary records are assessed. After the baseline
measurements the intervention group will receive tailored
counselling targeted to decrease sitting time by focusing on
commute and work time. The counselling regarding
leisure time is especially targeted to encourage toward family
physical activities such as visiting playgrounds and
non-built environments, where children can get diversified
stimulation for play and practice fundamental of motor
skills. The counselling will be reinforced during the first 6
months followed by a 6-month maintenance period.
Discussion: If shown to be effective, this unique family based
intervention to improve lifestyle behaviours in both
adults and children can provide translational model for
community use. This study can also provide knowledge
whether the lifestyle changes are transformed into relevant
biomarkers and self-reported health.
Trial registration number: ISRCTN: ISRCTN28668090
* Correspondence: [email protected]
1Neuromuscular Research Center, Department of Biology of
Physical Activity,
University of Jyväskylä, Jyväskylä, Finland
Full list of author information is available at the end of the
article

Finni et al. BMC Public Health 2011, 11:944
http://www.biomedcentral.com/1471-2458/11/944
© 2011 Finni et al; licensee BioMed Central Ltd. This is an
Open Access article distributed under the terms of the Creative
Commons
Attribution License
(http://creativecommons.org/licenses/by/2.0), which permits
unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
http://www.controlled-trials.com/ISRCTN28668090
mailto:[email protected]
http://creativecommons.org/licenses/by/2.0
Background
Parents of young children typically report low level of
physical activity and many can be classified as inactive
[1]. At the same time, parents face multidimensional
challenges to support physical activity in their children
[2]. While the physical activity guidelines stress the
importance of moderate-to-vigorous exercises in both
children and adults, evidence is merging that even small
amounts of physical activity can have health-related
benefits. One recent report shows that people who exer-
cise 15 min more/day have 14% lower risk of all-cause
mortality [3]. Conversely, every hour of TV viewing
adds all-cause mortality by 11% [4]. Further, a physically
active lifestyle has been shown to reduce genetic predis-
position to obesity by 40%, and thus also the risk for
obesity-related diseases [5].
Recently, daily inactivity time has been shown to be
independent from purposeful physical activity [6].

Therefore, not only adding physical activity but avoiding
inactivity has an important role in healthy lifestyle. Epi-
demiological studies have shown that sedentary time,
independent from exercise, predicts all-cause mortality
[7], obesity and type 2 diabetes [8,9], abnormal glucose
metabolism [10,11], the metabolic syndrome [12,13] and
cardiovascular disease [14]. In addition to the effects of
total sedentary time, the pattern in which it is accumu-
lated may be also important. Healy et al. [15] showed
that total number of breaks in sedentary time was asso-
ciated with lower waist circumference, triglycerides, and
2-h plasma glucose, independent of total sedentary time.
These authors suggested that breaking prolonged peri-
ods of sitting might be a valuable addition to the public
health recommendations. Although the evidence pro-
vides support to the importance of avoiding prolonged,
uninterrupted periods of sitting for cardiovascular health
in adults, further evidence from intervention trials is
required.
Promotion of sitting begins at schools at the latest and
increasing number of evidence show inactivity-related
symptoms also in adolescents and children [16]. Inter-
estingly, the amount of parent’s sedentary time, nor
their individual physical activity, seems to transfer to
their children [17,18]. On the other hand, parent’s and
siblings’ participation in physical activity with a child
and reinforcement for physical activity seems to be
important [19]. Thus, while our society supports sitting
and motorizing every action, promoting physically active
lifestyle as early as from childhood is crucial.
Based on Bandura’s [20] social cognitive theory, a
child learns by imitating and copying other people and
by making own reasons founded on these social situa-
tions. Own parents and siblings act as one of the most

powerful role models to a young child. Thus, the family
environment is in key position influencing child’s
physical activity habits taking shape in the childhood
[19,21-23].
The effective family interventions focused on increas-
ing physical activity in children have prompted parents
to set specific goals for changes in physical activity
behaviour and to self-monitor behaviour change pro-
gress and to identify barriers faced. It has also been
important that behaviour change techniques have
spanned the spectrum of behaviour change process [24].
Parents have been shown to be motivated to their role
as important models, if they receive knowledge why
physical activity (PA) is important to their children.
They also seem to profit from concrete ideas and model
how to promote physical activity with their children
[23].
For young children the parent’s encouragement
toward outdoor play seem to be essential [25] because
the amount of outdoor play correlates with overall phy-
sical activity [26,27]. Further, playing outdoors and
intensity level of moderate to vigorous physical activity
(MVPA) correlates with fundamental motor skills (FMS)
[28-30]. The more children are physically active the bet-
ter FMS they have and the more possibilities they have
to be physically active [31]. Good FMS during childhood
predict physical activity and physical fitness during ado-
lescence and later in adulthood [29,32,33]. On the other
hand, poor motor skills or clumsiness predicts low phy-
sical activity and poor physical fitness [34].
The purpose of this study is to find out the effects of
a tailored family based intervention on the activity level

of both parents and their children. In parents with a
sedentary job we investigate whether simple actions to
reduce time in sitting position are viable and whether
they have health-related benefits. Work-related inactivity
is one important aspect of the present study, since in
Finland 46% of women and 51% of men sit daily at least
6 h [35]. At the same time, we examine whether the
counselling delivered to parents on the importance of
children’s physical activity transfers to the increase in
children’s physical activity level and hereby facilitates
development of FMS.
The purpose of this article is to describe the design,
setting, recruitment process, methodology and interven-
tion of this randomized controlled trial.
Methods
Design
Parallel-group randomized controlled intervention trial
(RCT) design is used with one intervention group and
one control group of adults and their children. Adults in
the intervention group receive tailored counselling to
decrease time in sitting position and to increase non-
exercise daily activity. This counselling is also targeted
to encourage parents to increase their children’s time
Page 2 of 8
spent outdoors and to increase children’s intensity of
activity toward MVPA level. Parents are also helped to
recognize the amount of children’s indoor activities,

especially sedentary behaviour like TV viewing and play-
ing video games.
An ethics approval for the project has been received
from the Ethics committee of Central Finland Health
Care District on March 25, 2011 (Dnro 6U/2011).
Before signing consent, an info meeting is held where
the subjects are informed about the procedures, risks
and benefits of the study, and where they can ask ques-
tions. Subjects are volunteers with right to withdraw
from the study at any time without consequences. Con-
sent for the children to participate in the study will be
signed by both legal guardians. The study is conducted
according to Declaration of Helsinki and good scientific
practice.
Subjects
Eligible participants are parents with work that primarily
includes sitting and their children of 3-8 years of age.
Inclusion criteria: healthy men and women with children
3-8 years old, and having an occupation where they self-
reportedly sit more than 50% of their work time. Chil-
dren in all-day day-care in kindergarten at least 10 days
per month or in the first grade in primary school. Exclu-
sion criteria: self-reported chronic, long-term musculos-
keletal disease or progressive neurological disease,
diagnosed cardiovascular or metabolic disease with regu-
lar medication, families with pregnant mother at base-
line and BMI > 35. Children with a developmental
disorder or other disorders delaying motor development
are excluded.
Sample size
In the entire project we aim to see the main effect of
the intervention as reduced inactivity time in adults.
Sample size was calculated by assuming that the inter-

vention could induce a decrease in the longest muscle
inactivity periods from 6.37 ± 3.42 min to 5.22 ± 2.59
min in persons sitting 50-100% of their work time. This
assumption was based on our data from previous project
[36] where a mean duration of five longest continuous
inactivity periods was measured from 84 people with
self reported sitting (%) during work. A sample size of
86 in both groups would have 80% power to detect this
18% difference at the level of a = 0.05. Estimating a
small cluster effect (1.133) [37] and drop outs a sample
size of 100 in both groups was reached. However, since
we also aim to show health effects of the intervention,
we used changes in plasma triglycerides as an example
of potential metabolic changes to estimate the power of
the design with 100 subjects. Olsen et al. [38] reported
triglycerides to change with 14 days reduction in
ambulatory activities from 617 ± 56 μmol/l to 647 ± 47
μmol/l. With the assumption that this effect is reversible
within a year with increased ambulatory activity, a sam-
ple size of 100 in both groups will have 98.4% power to
detect this change. In addition, while conventional
thinking holds that sample size is the primary adjustable
variable for increasing statistical power, effect sizes
depend on phenotypic definition and will likely increase
the closer one connects to the underlying biochemistry
[39]. Since the metabonomic analysis provides more
detailed phenotype characterization this translates to
even greater statistical power.
Setting and randomization
The recruitment is performed in the city of Jyväskylä,
Finland, by delivering advertisements to parents via kin-
dergartens and primary schools which have been pre-
randomized to control and intervention groups after
balancing different environmental and socioeconomic

regions within the city. Jyväskylä with 130,000 residents
and surface area of 1171 km2 has relatively small city
centre and suburbs are characterized with proximity of
lakes and forests. In the city opportunities for active
commute using built-in bike paths and sidewalks are
numerous although winter with snow creates challenges
for a year-around commute. Also many of the kinder-
gartens and primary schools have the possibility to uti-
lize the natural environments in their daily program.
The compulsory education begins in August at the same
year children have their seventh birthday. Before the first
school year, 96% of Finnish children participate to a year-
long voluntary preschool [40]. The average enrolment rate
of children aged 3-5 years in childcare and early education
services is 74% in Finland which is slightly lower than
average of 77% in OECD-countries [41]. Kindergarten tea-
chers have the pedagogical responsibility of children’s
early education and their proficiency requirement is 3-5-
years of university or polytechnic education.
The recruitment started in April 2011 with five kin-
dergartens that resulted in about 16% response rate.
Based on this initial result we estimated that randomiza-
tion of 32 kindergartens and schools would be required
to reach required sample size. In the randomization the
areas within the city centre and in different types of
suburbs were balanced. Regarding socioeconomic
regions, information from city registry was used to bal-
ance the areas. Thus, in each type of environment and
socioeconomic region there were to be two or more kin-
dergartens and/or primary schools that were then ran-
domized into control and intervention groups.
Intervention
Tailored counselling by research personnel (TF, AL, AP)

is targeted to decrease time in sitting position and
Page 3 of 8
increase non-exercise activity during workdays (com-
mute and work time) and in leisure time (evenings and
weekends) in adults. A common 30-min lecture is given
to maximum of six participants accompanied with face-
to-face discussions. The lecture contains research-based
justifications for physical activity recommendations,
effects of physical activity and, specifically for adults,
information why reduction in sitting time can be
beneficial.
In the discussions the subjects will be first asked to
describe their current way of commute to work and to
think of feasible ways of increasing active modes of
commute. Then goals set by the subject regarding the
mode and frequency of the change (e.g. Instead of tak-
ing the bus I will bicycle to work i) sometimes, ii) once
a week, iii) several days a week, iv) every day) will be
marked down to a form.
Second, the work time will be discussed and the sub-
jects are asked to identify items that can be modulated,
e.g. standing in coffee breaks, walking about while talk-
ing to phone, breaking long periods of sitting, taking a
walk before lunch etc. Third, considering leisure time
(both weekdays and weekends) is especially focused to
encourage families to spend time together in nature and
non-built environments, playgrounds and parks where

children can get diversified stimulation for play. In dis-
cussions the parents are also encouraged, if needed, to
reinforce children to play outdoors with peers, to
acquire motivational equipment for play, to maintain
summertime physical activity level and to utilize the
material given. Moreover, leisure time habits will be
asked and changes in habits such as walking to grocery
shop instead of driving by car or walking stairs instead
of taking the lift are discussed.
The items that the subjects themselves suggest to
modify will be written down and an agreement docu-
ment will be signed by the subject and researcher to
confirm the intended changes in behaviour.
During counselling, material for break exercises during
work time, children’s indoor and outdoor activities and
local places for hiking are delivered. Documents
“License to Move” and a fair copy of the agreement are
given. The subjects are also informed about project
web-page [42] containing motivational material for
intervention group which will be regularly updated.
The counselling is reinforced by a phone call after 1
and 5 months during which the compliance is asked
and modifications to the agreement can be made. Dur-
ing the phone discussions execution (both compliance
and barriers) of each of the goals are asked and modi-
fied when required. In addition, motivational e-mails
with information on health promotion by life-style phy-
sical activity for adults and illustrative tips to increase
physical activity and play developing FMS in children
are sent monthly. This reinforcement period lasts for 6
months. At Midline the subjects in the intervention
group will be given individual feedback of their daily
inactivity and activity times (from Baseline muscle activ-

ity measurements). Feedback of children’s motor skills
with motivational tips will also be given at Midline.
After the Midline there will be no researcher contact
with the subjects except for the 9 month measurements.
The control group will not receive the counselling
intervention but will undergo the measurements simi-
larly as the intervention group. After 12 month-mea-
surements the control group will be invited to a
feedback and counselling session where they have the
possibility to receive the same information as the inter-
vention group.
Study protocol and outcomes
Protocol for adults
The timeline of the study is shown in Figure 1. Baseline,
Midline and Endline measurements contain fasting blood
samples, anthropometry, questionnaires, strength mea-
surements and functional tasks. These measurements
1 normal day
according to
agreed
intervention
Tailored
counseling
Endline
3 months
1 wk PA
measurement
1 wk PA

measurement
1 wk PA
measurement
1 wk PA
measurement
6 months 12 months
Midline
Baseline 0 months 9 months
1 wk PA
measurement
Figure 1 Timeline of the project 1 week physical activity
measurement (1 week PA measurement) refers to accelerometer
measurements from both adults and children. In adults also
heart rate for 3 days and 2 nights with orthostatic test in the
morning are
assessed at these time points as well as fasting blood samples.
At baseline, midline and endline questionnaires, body
composition, daily activity
tasks are assessed. At these 3 time points PA measurements
include 1-day thigh muscle activity measurements.
Page 4 of 8
initiate 1 week physical activity recordings during normal

daily life. Counselling takes place few weeks after the
baseline, and the maximum effect of counselling on phy-
sical activity will be tested about 1 week after the coun-
selling (0 months). At this time data will also be collected
from control group who is told that this assessment
relates to reliability and they are to live a normal life
similarly as in all the other measurements. The measure-
ments at 3 and 9 months include only fasting blood sam-
ple, heart rate and accelerometer measurements.
Baseline, Midline and Endline measurements initiate
physical activity monitoring during normal daily life
(Figure 2). The subjects will wear shorts measuring
muscle activity for 1 day, heart rate device for 3 days
and 2 nights and accelerometer for 7 days. During the
days of physical activity data collection the subjects fill
in physical activity diaries with information on sleep
quality, alcohol usage and particularly stressful
situations.
Outcome measures from adults
Short term main outcome is the PA behaviour which will
be assessed with electromyography (EMG) and acceler-
ometer. PA measured using EMG during 1 day at base-
line will be compared to the daily measurements done
after counselling. During this day, that is similar work-
day as in the baseline, the subjects are instructed to
carefully execute the agreed changes in their PA beha-
viour allowing examination of maximal effect of the
counselling. In the control group, this measurement will
serve repeatability assessment. Commute time, work
time and leisure time will be analyzed separately.
EMG will be measured using shorts with embedded
textile electrodes (Myontec Ltd., Kuopio, Finland) mea-
suring activity from left and right quadriceps and ham-
string muscles (Figure 3). The measurement system has

been tested for validity, repeatability and feasibility in
our laboratory [43]. The data is collected to a 52 g
module in the waist in raw form with sampling fre-
quency of 1000 Hz. The raw EMG signal is rectified and
averaged over 100 ms non-overlapping intervals before
storing the data into the module. The daily EMG level
will be normalized to that during isometric maximum
voluntary contraction. Inactivity threshold is individually
determined as 0.9% of the standing EMG level. Several
activity and inactivity parameters will be analyzed using
custom made Matlab program including burst analysis
[44].
Acceleration Triaxial acceleration will be collected dur-
ing all the measurements (Alive heart monitor, Alive
technologies Ltd., Australia). We will analyze the com-
monly used activity counts but also use signal half
power and histograms to describe changes in PA beha-
viour. Alive monitor can simultaneously collect also
electrocardiogram from which the heart rate variability
and related stress indices are calculated using Hyvin-
vointianalyysi-software (Firstbeattechnologies Ltd.,
Finland).
Long term main outcomes are health-related indices
and maintenance of the behavioural change. Changes in
physical activity behaviour will be assessed using EMG,
accelerometer and questionnaires. The health indices to
be collected 5 times a year are fasting blood samples
and diurnal heart rate (Figure 1). Blood pressure and
body composition with dual energy x-ray absorptiometry
(DXA, LUNAR Prodigy, GE Healthcare) will be mea-
sured at Baseline, Midline and Endline in fasting
condition.
Venous blood samples for whole blood count, blood

lipids and glucose will be taken in standardized fasting
conditions in the mornings 7-9 am. Samples will be ana-
lyzed using standard methods in clinical use. Serum will
be stored at-80°C for later analysis of the entire serum
metabolome via NMR spectroscopy. This methodology
provides information on ~150 primary metabolic
Figure 2 Timeline of baseline measurements at baseline the
fasting laboratory tests were followed by 1 day recording of
muscle EMG
activity, diurnal measurements of heart rate for 3 days and
accelerometer measurements for 7 days.
Page 5 of 8
measures and ~100 derived variables with clear bio-
chemical interpretation and significance. The directly
measured metabolites include lipoprotein subclass distri-
bution with 14 subclasses quantified including particle
concentrations and individual subclass lipids, low-mole-
cular-weight metabolites such as amino acids, ketone
bodies, and creatinine, and detailed molecular informa-
tion on serum lipid extracts including free and esterified
cholesterol, sphingomyelin, degree of saturation and ω-3
fatty acids. Derived variables include selected ratios of
metabolites implicated in lipolysis, proteolysis, ketogen-
esis and glycolysis as well as reagents and products of
enzymatic reactions and measures obtained with the
extended-Friedewald formula, eg. apolipoprotein A-I
and B [45]. Further details of the NMR spectroscopy,
quantification data analyses, as well as the full metabo-

lite identifications can be found from [46,47].
Heart rate will be measured with the same device as
acceleration. The subjects wear the monitors for 3 days
and 2 nights. In the morning the subjects will perform
an orthostatic test. Previously, heart rate variability from
this test has been shown to correlate with self-reported
stress [48]. Both time domain and frequency domain
analysis of heart rate variability will be performed,
where high frequency band has special meaning in
determining cardiovascular health [49].
Questionnaires Background information of medica-
tion, chronic and acute diseases will be assessed in the
screening process. Dietary records for 3 weekdays and
1 weekend day will be kept at Baseline and Endline
and 1 day diaries at 3, 6 and 9 months to evaluate
whether similar diet has been maintained during the
year as assumed. The subjects will also fill in the fol-
lowing questionnaires: Socio-economic background
(Baseline), 12-month physical activity (Baseline, End-
line, modified from [50], quality of life (RAND-36,
Baseline, Endline), Work Ability Index (Baseline, End-
line) and Occupational Stress Questionnaire (Baseline,
Midline, Endline) (Finnish Institute of Occupational
Health). Physical fitness will be assessed with non-exer-
cise questionnaire (Baseline, Endline) and additional
details of physical activity intensity and frequency will
be asked at all time points. Family-Influence Model
questionnaire [19] will be assessed in Baseline, Midline
and Endline.
Protocol for children
At Baseline, 3, 6, 9 and 12 months, children will be
measured for physical activity level during waking hours
for 6 days. During the days of physical activity measure-

ments the parents will mark down to the children’s
diary the times the accelerometer were put on and
taken off. In addition, parents are asked to subjectively
estimate the amount of children’s MVPA time after day-
care or school. At Baseline, Midline and Endline chil-
dren will also be measured for FMS.
Outcome measures from children
Physical activity will be measured using triaxial accel-
erometer (X6-1A USB Accelerometer, Gulf Coast Data
Concepts, USA) which is placed on children’s waist
with firmly fitted elastic band. The time at day-care or
school will be analyzed separately from leisure time.
Extracted variables will include commonly used activity
counts based on thresholds [51], histograms and half
power of the signal. FMS will be tested using Körper-
koordinations Test fur Kinder (KTK) [52] for balance
and motor coordination and using APM object control
test [53]. APM is a widely used motor skill test for
children in Finland. FMS tests are conducted as part of
daily activities in kindergartens, schools and, in some
cases, in the laboratory of Department of Biology of
Physical Activity.
Statistical analysis
The maximal effect of counselling will be tested using
mixed model ANOVA comparing the control and inter-
vention groups at baseline and at 0 months. Repeated
measures ANOVA will be used to determine effects of
intervention with an intention-to-treat principle. Mixed
models are used in case of missing data points and to
examine the clustering effect as well as to examine the
relationships between physical activity and health-related
indices. Self-organizing maps (SOM) will be applied for
multi-metabolic understanding of metabolic interrela-
tionships and pathways [54,55].

Figure 3 EMG shorts used to collect muscle EMG activity from
thigh muscles.
Page 6 of 8
Discussion
This study is unique in implementing intervention to
both parents and their children at the same time. The
intervention focusing on reducing inactivity rather than
increasing physical activity is a rather new concept and
the actual execution by the adults is likely to determine
the significance of this study. For the intervention group
we have carefully designed a motivating environment
with face-to-face discussions where the goals will be set
by the subjects themselves and the commitment is for-
malized by signing an agreement. This type of interven-
tion has been shown to be very effective [56].
To reliably extract the possible changes in physical
activity the present study utilizes both conventional
(such as activity counts from accelerometer) and novel
methods (histograms and signal half power from accel-
erometer and EMG) for more accurate determination of
physical activity intensity and durations. Further, the
measurements are done long enough periods 5 times
during the year to assess the altered behaviour. Whether
the altered behaviour in adults is reflected to health, it
is postulated that the metabonomics analysis is sensitive
enough to detect even subtle changes that would not be

visible in conventional clinical markers.
There are challenges in this trial. Firstly, the compli-
ance cannot be foreseen. The subject group is at very
busy stage of life making careers with small children
and a year-long commitment may result in considerable
attrition rates. Further, seasonal variations in Finland
with warm summer and cold winter with snow may
show a greater effect on physical activity and health
indexes than the low threshold intervention. We have
considered this effect by performing the baseline mea-
surements sequentially in spring and fall seasons.
Overall, this study has the potential to provide novel
and timely information on the cause-effect relationships
of low threshold physical activity intervention. However,
the effectiveness of the intervention can only be assessed
after the end of the study.
Abbreviations
ECG: Electrocardiogram; EMG: Electromyography; FMS:
Fundamental motor
skills; MVPA: Moderate-to-vigorous physical activity; NMR:
Nuclear magnetic
resonance; PA: Physical activity.
Acknowledgements
This study is funded by Ministry of Education and Culture,
Finland (DNRO
42/627/2010). We thank Dr. Teemu Pullinen, Prof. Mika Ala-
Korpela, Dr. Pasi
Soininen and Mr. Antti J. Kangas for their contributions to this
study design
and methodology.
Author details

1Neuromuscular Research Center, Department of Biology of
Physical Activity,
University of Jyväskylä, Jyväskylä, Finland. 2Department of
Physical Education,
University of Jyväskylä, Jyväskylä, Finland. 3Gerontology
Research Centre,
Department of Health Sciences, University of Jyväskylä,
Jyväskylä, Finland.
Authors’ contributions
TF, AS and SS initially designed the study and obtained funding
for the
research. All authors contributed to developing the protocols
and
intervention materials. TF, AL and AP drafted the manuscript
and all authors
were involved in revising it for intellectual content and have
given final
approval of the version to be published. All authors read and
approved the
final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 2 December 2011 Accepted: 20 December 2011
Published: 20 December 2011
References
1. Rovio E, Hakonen H, Kankaanpää A, Eskola J, Hakamäki M,
Tammelin T,
Helakorpi S, Uutela A, Havas E: Physically inactive adults in
Finland–
Identification of the subgroups. Liikunta & Tiede 2009,
46(6):26-33.

2. Dwyer J, Needham L, Simpson JR, Heeney ES: Parents report
intrapersonal,
interpersonal, and environmental barriers to supporting healthy
eating
and physical activity among their preschoolers. Appl Physiol
Nutr Metab
2008, 33(2):338-346.
3. Wen CP, Wai JP, Tsai MK, Yang YC, Cheng TY, Lee MC,
Chan HT, Tsao CK,
Tsai SP, Wu X: Minimum amount of physical activity for
reduced
mortality and extended life expectancy: a prospective cohort
study. The
Lancet 2011, 378(9798):1244-1253.
4. Dunstan DW, Barr ELM, Healy GN, Salmon j, Shaw JE,
Balkau B, Magliano DJ,
Cameron AJ, Zimmet PZ, Owen N: Television viewing time and
mortality:
the Australian diabetes, obesity and lifestyle study (AusDiab).
Circulation
2010, 121:384-391.
5. Li S, Zhao JH, Luan J, Ekelund U, Luben RN, Khaw KT,
Wareham NJ, Loos RJ:
Physical activity attenuates the genetic predisposition to obesity
in
20,000 men and women from EPIC-norfolk prospective
population
study. PLoS Med 2010, 7(8):e1000332.
6. Burton NW, Khan A, Brown WJ, Turrell G: The association
between
sedentary leisure and physical activity in middle-aged adults. Br

J Sports
Med 2011, DOI:10.1136/bjsm.2010.081430.
7. Katzmarzyk PT, Church TS, Craig CL, Bouchard C: Sitting
time and mortality
from all causes, cardiovascular disease, and cancer. Med Sci
Sports Exerc
2009, 41(5):998-1005.
8. Hu FB, Leitzmann MF, Stampfer MJ, Colditz GA, Willett
WC, Rimm EB:
Physical activity and television watching in relation to risk for
type 2
diabetes mellitus in men. Arch Intern Med 2001, 161:1542-
1548.
9. Hu FB, Li TY, Colditz GA, Willett WC, Manson JE:
Television watching and
other sedentary behaviors in relation to risk of obesity and type
2
diabetes mellitus in women. JAMA 2003, 289(14):1785-1791.
10. Dunstan DW, Salmon J, Owen N, Armstrong T, Zimmet PZ,
Welborn TA,
Cameron AJ, Dwyer T, Jolley D, Shaw JE, AusDiab Steering
Committee:
Physical activity and television viewing in relation to risk of
undiagnosed abnormal glucose metabolism in adults. Diabetes
Care 2004,
27:2603-2609.
11. Dunstan DW, Salmon J, Healy GN, Shaw JE, Jolley D,
Zimmet PZ, Owen N,
AusDiab Steering Committee: Association of television viewing
with
fasting and 2-h postchallenge plasma glucose levels in adults

without
diagnosed diabetes. Diabetes Care 2007, 30:516-522.
12. Dunstan DW, Salmon J, Owen N, Armstrong T, Zimmet PZ,
Welborn TA,
Cameron AJ, Dwyer T, Jolley D, Shaw JE, AusDiab Steering
Committee:
Association of TV viewing and physical activity with the
metabolic
syndrome in Australian adults. Diabetologia 2005, 48:2254-
2261.
13. Kronenberg F, Pereira MA, Schmitz MK, Arnett DK,
Evenson KR, Crapo RO,
Jensen RL, Burke GL, Sholinsky P, Ellison RC, Hunt SC:
Influence of leisure
time physical activity and television watching on
atherosclerosis risk
factors in the NHLBI family heart study. Atherosclerosis 2000,
153:433-443.
14. Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE,
Zimmet PZ, Owen N:
Breaks in sedentary time: beneficial associations with metabolic
risk.
Diabetes Care 2008, 31:661-666.
15. Ford ES, Kohl HW, Mokdad AH, Ajani UA: Sedentary
behavior, physical
activity, and the metabolic syndrome among U.S. adults. Obes
Res 2005,
13:608-614.
16. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ,
Larouche R, Colley RC,
Goldfield G, Gorber SC: Systematic review of sedentary

behavior and
Page 7 of 8
http://www.ncbi.nlm.nih.gov/pubmed/18347689?dopt=Abstract
health indicators in school-aged children and youth. Int J Behav
Nutr Phys

Act 2011, 21(8):98.
17. Ianotti RJ, Sallis JF, Chen R, Broyles SL, Elder JP, Nader
PR: Prospective
analyses of relationships between mothers’ and children’s
physical
activity. J Phys Act Health 2005, 2:16-34.
18. Jago R, Fox KR, Page AS, Brockman R, Thompson JL:
Parent and child
physical activity and sedentary time: do active parents foster
active
children? BMC Public Health 2010, 10(194):1-9.
19. Cleland V, Timperio A, Salmon J, Hume C, Telford A,
Crawford D: A
longitudinal study of the family physical activity environment
and
physical activity among youth. Am J Health Promotion 2011,
25(3):159-168.
20. Bandura A: Social foundations of thought and action: A
social cognitive
theory Englewood Cliffs: Prentice-Hall; 1986.
21. Fogelholm M, Nuutinen O, Pasanen M, Myöhänen E, Säätelä
T: Parent-child
relationship of physical activity patterns and obesity. Int J Obes
Relat
Metab Disord 1999, 23(12):1262-1268.
22. Simonen RL, Pérusse L, Rankinen T, Rice T, Rao DC,
Bouchard C: Familial
aggregation of physical activity levels in the Québec family
study. Med
Sci Sports Exerc 2002, 34(7):1137-1142.

23. Sääkslahti A: In Effects of physical activity intervention on
physical activity and
motor skills and relationships between physical activity and
coronary heart
disease risk factors in 3-7-year-old children. Volume 104.
University of
Jyväskylä;2005, Studies in Sport, Physical Education and
Health.
24. Golley RK, Hendrie GA, Slater A, Corsini N: Interventions
that involve
parents to improve children’s weight-related nutrition intake
and
activity patterns–what nutrition intake and activity targets and
behavior
change techniques are associated with intervention
effectiveness? Obes
Rev 2011, 12(2):114-130.
25. Sääkslahti A, Numminen P, Salo P, Tuominen J, Helenius H,
Välimäki I:
Effects of a 3-year intervention on children’s physical activity
from age
4-7. Pedc Exer Sci 2004, 16(2):167-180.
26. McWilliams C, Ball SC, Benjamin SE, Hales D, Vaughn A,
Ward DS: Best-
practice guidelines for physical activity at child care. Pediatrics
2009,
124(6):1650-1659.
27. Sallis JF, Prochaska JJ, Taylor WC: A review of correlates
of physical
activity of children and adolescents. Med Sci Sports Exer 2000,
32(5):963-975.

28. Sääkslahti A, Numminen P, Niinikoski H, Rask-Nissilä L,
Viikari J, Tuominen J,
Välimäki I: Is physical activity related to body size,
fundamental motor
skills, and CHD risk factors in early childhood? Pediatr Exerc
Sci 1999,
11:327-340.
29. Barnett L, Van Beurden E, Morgan P, Brooks L, Beard J:
Does childhood
motor skill proficiency predict adolescent fitness? Med Sci
Sports Exer
2008, 40(12):2137-2144.
30. Raudsepp L, Päll P: The relationship between fundamental
motor skills
and outside-school physical activity of elementary school
children. Ped
Exer Sci 2006, 16:426-435.
31. Stodden D, Goodway J, Langendorfer S, Roberton M,
Rudisill M, Garzia C,
Garzia L: A developmental perspective on the role of the motor
skill
competence in physical activity: an emergent relationship.
QUEST 2008,
60:290-306.
32. Lopes VP, Rodrigues LP, Maia JAR, Malina RM: Motor
coordination as
predictor of physical activity in childhood. Scand J Med Sci
Sports 2009,
21(5):663-669.
33. Malina RM: Tracking of physical activity and physical

fitness across the
lifespan. Res Quart Exer Sport 1996, 67(3):S48-57.
34. Kantomaa M, Purtsi J, Taanila A, Remes J, Viholainen H,
Rintala P, Ahonen T,
Tammelin T: Suspected motor problems and low preference for
active
play in childhood are associated with physical inactivity and
low fitness
in adolescence. PLoS ONE 2011, 6(1):e14554.
35. Sjöström M, Oja P, Hagströmer M, Smith BJ, Bauman A:
Health-enhancing
physical activity across European Union countries: the
Eurobarometer
study. J Public Health 2006, 14:291-300.
36. Muscle loading during physical activity and normal daily
life: correlates
with health and well being (EMG 24). ,
https://staff.jyu.fi/Members/finni/
EMG24.
37. Campbell MK, Elbourne DR, Altman DG, CONSORT group:
Consort
statement: extension to cluster randomised trials. BMJ 2004,
328:702-708.
38. Olsen RH, Krogh-Madsen R, Thomsen C, Booth FW,
Pedersen BK: Metabolic
responses to reduced daily steps in healthy nonexercising men.
JAMA
2008, 299:1261-1263.
39. Ala-Korpela M, Kangas AJ, Inouye M: Genome-wide
association studies

and systems biology: together at last. Trends in genetics 2011,
27(12):493-498.
40. The proceedings of Ministry of the Interior 27/2004.
[http://www.
intermin.fi/julkaisu/272004].
41. OECD family database 2008.
[http://www.oecd.org/els/social/family/
database].
42. Perheliikunta. Tukimateriaalia perheliikunnan edistämiseksi.
[http://
perheliikunta.nettisivu.org].
43. Finni T, Hu M, Kettunen P, Vilavuo T, Cheng S:
Measurement of EMG
activity with textile electrodes embedded into clothing. Physiol
Meas
2007, 28:1405-1419.
44. Kern DS, Semmler JG, Enoka RM: Long-term activity in
upper–and lower–
limb muscles of humans. J Appl Physiol 2001, 91:2224-2232.
45. Niemi J, Mäkinen VP, Heikkonen J, Tenkanen L, Hiltunen
Y, Hannuksela ML,
Jauhiainen M, Forsblom C, Taskinen MR, Kesäniemi YA,
Savolainen MJ,
Kaski K, Groop PH, Kovanen PT, Ala-Korpela M: Estimation of
VLDL, IDL,
LDL, HDL2, apoA-I, and apoB from the Friedewald inputs—
apoB and
IDL, but not LDL, are associated with mortality in type 1
diabetes. Ann
Med 2009, 41(6):451-61.

46. Soininen P, Kangas AJ, Würtz P, Tukiainen T, Tynkkynen
T, Laatikainen R,
Järvelin MR, Kähönen M, Lehtimäki T, Viikari J, Raitakari OT,
Savolainen MJ,
Ala-Korpela M: High-throughput serum NMR metabonomics for
cost-
effective holistic studies on systemic metabolism. Analyst 2009,
134(9):1781-1785.
47. Inouye M, Kettunen J, Soininen P, Silander K, Ripatti S,
Kumpula LS,
Hämäläinen E, Jousilahti P, Kangas AJ, Männistö S, Savolainen
MJ, Jula A,
Leiviskä J, Palotie A, Salomaa V, Perola M, Ala-Korpela M,
Peltonen L:
Metabonomic, transcriptomic, and genomic variation of a
population
cohort. Mol Syst Biol 2010, 21(6):441.
48. Hynynen E, Konttinen N, Kinnunen U, Kyröläinen H, Rusko
H: The incidence
of stress symptoms and heart rate variability during sleep and
orthostatic test. Eur J Appl Physiol 2011, 111(5):733-741.
49. Tulppo MP, Hautala AJ, Mäkikallio TH, Laukkanen RT,
Nissilä S, Hughson RL,
Huikuri HV: Effects of aerobic training on heart rate dynamics
in
sedentary subjects. J Appl Physiol 2003, 95:364-372.
50. Lakka TA, Salonen TJ: The physical activity questionnaires
of the Kuopio
Ischemic Heart Disease Study (KIDH). Med Sci Sports Exerc
1997, 29:
S46-S58.

51. Van Cauwenberghe E, Labarque V, Trost SG, De
Bourdeaudhuij I, Cardon G:
Calibration and comparison of accelerometer cut points in
preschool
children. Int J Ped Obes 2011, 6(2-2):582-589.
52. Schilling F: Koordinationtest für kinder KTK manual
Göttingen: Belzt test;
2000.
53. Numminen P: Alle kouluikäisten lasten havaintomotorisia ja
motorisia
perustaitoja mittaavan APM-testistön käsikirja. Liikunnan ja
kansanterveyden julkaisuja 98 Jyväskylä; 1995.
54. Kumpula LS, Mäkelä SM, Mäkinen VP, Karjalainen A,
Liinamaa JM, Kaski K,
Savolainen MJ, Hannuksela ML, Ala-Korpela M:
Characterization of
metabolic interrelationships and in silico phenotyping of
lipoprotein
particles using self-organizing maps. J Lipid Res 2010,
51(2):431-9.
55. Mäkinen VP, Soininen P, Forsblom C, Parkkonen M,
Ingman P, Kaski K,
Groop PH, Ala-Korpela M: 1H NMR metabonomics approach to
the
disease continuum of diabetic complications and premature
death. Mol
Syst Biol 2008, 4:167.
56. Conn VS, Hafdahl AR, Mehr DR: Interventions to Increase
Physical Activity
Among Healthy Adults: Meta-Analysis of Outcomes. Am J

Public Health
2011, doi:10.2105/AJPH.2010.194381.
Pre-publication history
The pre-publication history for this paper can be accessed here:
http://www.biomedcentral.com/1471-2458/11/944/prepub
doi:10.1186/1471-2458-11-944
Cite this article as: Finni et al.: A family based tailored
counselling to
increase non-exercise physical activity in adults with a
sedentary job
and physical activity in their young children: design and
methods of a
year-long randomized controlled trial. BMC Public Health 2011
11:944.
Page 8 of 8

http://www.intermin.fi/julkaisu/272004
http://www.intermin.fi/julkaisu/272004
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http://www.oecd.org/els/social/family/database
http://perheliikunta.nettisivu.org
http://perheliikunta.nettisivu.org
http://www.biomedcentral.com/1471-
2458/11/944/prepubAbstractBackgroundMethodsDiscussionTrial
registration numberBackgroundMethodsDesignSubjectsSample

sizeSetting and randomizationInterventionStudy protocol and
outcomesProtocol for adultsOutcome measures from
adultsProtocol for childrenOutcome measures from
childrenStatistical analysisDiscussionAcknowledgementsAuthor
detailsAuthors' contributionsCompeting interestsReferencesPre-
publication history
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Neuroscience and Biobehavioral Reviews 35 (2011) 621–634
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c
a t e / n e u b i o r e v
eview
he influences of environmental enrichment, cognitive
enhancement, and
hysical exercise on brain development: Can we alter the
developmental
rajectory of ADHD?
effrey M. Halperin a,b,∗ , Dione M. Healey c
Department of Psychology, Queens College of the City
University of New York, USA
Department of Psychiatry, Mount Sinai School of Medicine,
New York, USA
Department of Psychology, University of Otago, New Zealand
r t i c l e i n f o
rticle history:
eceived 21 February 2010

eceived in revised form 11 July 2010
ccepted 28 July 2010
eywords:
DHD
eural development
a b s t r a c t
Attention-deficit/Hyperactivity Disorder (ADHD) is
characterized by a pervasive pattern of developmen-
tally inappropriate inattentive, impulsive and hyperactive
behaviors that typically begin during the
preschool years and often persist into adulthood. The most
effective and widely used treatments for
ADHD are medication and behavior modification. These
empirically-supported interventions are gen-
erally successful in reducing ADHD symptoms, but treatment
effects are rarely maintained beyond the
active intervention. Because ADHD is now generally thought of
as a chronic disorder that is often present
well into adolescence and early adulthood, the need for
continued treatment throughout the lifetime is
eurocognitive functioning
nvironmental enrichment
xercise
reatment
both costly and problematic for a number of logistical reasons.
Therefore, it would be highly beneficial
if treatments would have lasting effects that remain after the
intervention is terminated. This review
examines the burgeoning literature on the underlying neural
determinants of ADHD along with research
demonstrating powerful influences of environmental factors on
brain development and functioning.

Based upon these largely distinct scientific literatures, we
propose an approach that employs directed
play and physical exercise to promote brain growth which, in
turn, could lead to the development of
potentially more enduring treatments for the disorder.
© 2010 Elsevier Ltd. All rights reserved.
ontents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 622
2. Overview of evidence-based treatments for ADHD . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622
2.1. Key limitations of current evidence-based interventions for
ADHD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622
3. Neural substrates of ADHD . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 623
3.1. Neuroimaging evidence . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623
3.2. Neuropsychological findings . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624
4. Neurodevelopmental perspectives on ADHD . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624
5. Neural development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . 625
5.1. Environmental influences on neurodevelopment . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626
6. Cognitive training paradigms in children with ADHD . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627
6.1. Cognitive enhancement in the child’s social milieu . . . . . .
. . . . . . . . . .
7. Future directions for neurocognitively-based treatments of
ADHD . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . .
∗ Corresponding author at: Department of Psychology, Queens
College of the City Unive
el.: +1 718 997 3254; fax: +1 718 997 3218.
E-mail address: [email protected] (J.M. Halperin).
149-7634/$ – see front matter © 2010 Elsevier Ltd. All rights
reserved.
oi:10.1016/j.neubiorev.2010.07.006
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 628
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . 628
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 629
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 629
rsity of New York, 65-30 Kissena Blvd., Flushing, NY 11367,
USA.
dx.doi.org/10.1016/j.neubiorev.2010.07.006
http://www.sciencedirect.com/science/journal/01497634
http://www.elsevier.com/locate/neubiorev
mailto:[email protected]
dx.doi.org/10.1016/j.neubiorev.2010.07.006
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22 J.M. Halperin, D.M. Healey / Neuroscience
. Introduction
Attention-deficit/Hyperactivity Disorder (ADHD) is a chronic,
ighly prevalent neurodevelopmental disorder which affects as
any as 9% of school-age children (Pastor and Reuben, 2008).
ADHD
ypically emerges during the preschool years, persists through
dolescence and into adulthood for many afflicted individuals,
nd causes significant functional disability throughout the
lifespan
American Psychiatric Association Task Force on DSM-IV,
2000).
everal effective empirically-validated psychopharmacologic and
ehavioral interventions for ADHD are currently available. These
nterventions have been shown to ameliorate the core symptoms
f ADHD and to improve academic performance across a wide
array
f school-related measures. However, such gains are rarely main-
ained after the termination of treatment (Chronis et al., 2003,
004; Jensen et al., 2007), and relatively few individuals with
ADHD
eceive effective treatment throughout the full course of their
dis-

rder (Corkum et al., 1999; Jensen et al., 2007; MTA
Cooperative
roup, 1999, 2004; Perwien et al., 2004; Sanchez et al., 2005;
Weiss
t al., 2006; Molina et al., 2009). The need for continued
treatment
hroughout the lifetime is both costly and problematic for a num-
er of logistical reasons. Therefore, it would be highly beneficial
if
treatment could be employed that would have lasting effects
that
emain after the active intervention is terminated.
Recent research has begun to elucidate the underlying neu-
al (Casey et al., 1997; Castellanos, 2001; Makris et al., 2007;
chulz et al., 2004, 2005a; Seidman et al., 2005; Shaw et al.,
2006,
007a,b) and neurocognitive (Sergeant et al., 1999; van Mourik
et
l., 2005; Willcutt et al., 2005) determinants of ADHD.
Additionally,
distinct scientific literature, largely consisting of research with
nimals, indicates that an array of neurodevelopmental processes
acilitate efficient neurotransmission and are highly responsive
to
nvironmental influences. The bringing together of these
scientific
iteratures may provide valuable insights for the development of
ore enduring treatments for ADHD.
. Overview of evidence-based treatments for ADHD
Pharmacological interventions, most prominently stimulant

edication, and behavioral interventions in the forms of par-
nt training and contingency management in the classroom, are
onsidered the best-supported treatments for ADHD. These inter-
entions result in significant benefits for children with ADHD in
ultiple domains of functioning (Conners, 2002; Greenhill et al.,
999; Pelham et al., 1998; Pelham and Fabiano, 2008; Spencer
t al., 1996). For example, studies of behavioral parent train-
ng (BPT) for ADHD have demonstrated improvements in ADHD
ymptoms (Anastopoulos et al., 1993; Sonuga-Barke et al., 2001;
unningham et al., 1995), as well as co-occurring oppositional
roblems and impairment in children (Erhardt and Baker, 1990;
isterman et al., 1989, 1992). BPT also improves parental
function-
ng (e.g., decreased stress, enhanced competence) (Anastopoulos
t al., 1993; Pisterman et al., 1992; Sonuga-Barke et al., 2001).
oreover, behavior contingency management in the classroom
ields improvements in teacher reports of children’s functioning,
bserved behavior of children with ADHD in the classroom set-
ing, as well as better academic productivity (Abramowitz et al.,
987; Fabiano et al., 2007; Pelham et al., 1998; Hoffman and
DuPaul,
000). While data indicate that behavioral treatments are not, on
he group level, as effective as carefully-monitored stimulant
medi-
ation, the large Multimodal Treatment Study of ADHD (MTA
Study)
ound that their pure behavioral treatment group improved to a
imilar degree as their community treated control group, the
major-
ty of whom received medication treatment from their provider
utside of the study (MTA Cooperative Group, 1999).

iobehavioral Reviews 35 (2011) 621–634
Stimulant medication has been shown to improve functioning
in children with ADHD, with studies demonstrating
improvements
in core symptoms of ADHD, compliance, aggression, and
academic
productivity (Conners, 2002; Greenhill et al., 1999; Spencer et
al.,
1996). Effect-size calculations from both behavioral
interventions
and stimulant medication studies demonstrate that these inter-
ventions result in substantial improvements across domains of
functioning (Conners, 2002; Pelham and Fabiano, 2008).
Collec-
tively, compelling evidence indicates that behavioral
interventions
and stimulant medication improve the functioning of children
with
ADHD and, in some cases, have additional benefits for their
families.
However, noted below, are several limitations to these
treatments
which necessitate further investigation into alternative interven-
tions for children with ADHD.
2.1. Key limitations of current evidence-based interventions for
ADHD
Although clearly efficacious, there are limitations to current
evidence-based interventions. Stimulant medication is an easy
intervention to implement, but many parents prefer not to use
medication as a treatment for their child with ADHD (Pisecco et
al., 2001; Power et al., 1995). Moreover, a substantial number
of
children experience notable side effects with stimulant medica-

tion (MTA Cooperative Group, 2004; Swanson et al., 2006,
2007a;
Wigal et al., 2006) that may prohibit continued use. In addition,
recent concerns have been raised by the American Heart Associ-
ation (American Heart Association, 2008) suggesting
problematic
interactive effects of stimulant medication with underlying car-
diac conditions that may further limit the acceptability of
stimulant
medication for children with ADHD, or at least make it less
palatable
to parents. Behavioral interventions, in contrast, are more
palatable
but much more difficult to implement, generally quite costly,
and
may be less effective than stimulant medications (MTA
Cooperative
Group, 1999).
In addition, there are several limitations that both stimulant
medication and behavioral interventions share in common. First,
although both treatments are efficacious, the behavior of a
signif-
icant number of children with ADHD is not normalized by the
use
of these interventions (Hoza et al., 2005; Swanson et al., 2001).
Swanson et al. (2001) found that 32–64% of children continued
to
exhibit clinically significant levels of ADHD despite intensive
stim-
ulant medication and behavioral treatment regimens. Moreover,
Hoza et al. (2005) found that despite intensive treatment over
14
months, children continued to have difficulties in peer relation-
ships. Therefore, although children with ADHD may do
significantly

better with stimulant medication and/or behavioral interventions
relative to baseline, they still appear deviant relative to their
peers
in key areas of functioning.
Furthermore, treatment effects rarely persist past the point of
active dosing/implementation (Chronis et al., 2003, 2004). This
suggests that implementation of behavioral interventions and
stimulant medication temporarily suppresses behavioral
difficul-
ties and that these difficulties resurface when treatment is no
longer active. As such, there are no apparent changes in the
under-
lying deficits that produce the behavioral manifestations of
ADHD.
In addition, although ADHD is considered a chronic condi-
tion that impacts functioning across multiple settings, which
would thus require long-term treatment that impacts each
affected
setting, long-term adherence to both stimulant medication and
behavioral interventions is often poor. For instance, there is
considerable noncompliance with stimulant medication (MTA
Cooperative Group, 2004; Corkum et al., 1999; Jensen et al.,
2007;
Perwien et al., 2004; Sanchez et al., 2005; Weiss et al., 2006).
More
than half of children prescribed stimulant medication stop
receiv-
ing treatment within a school year (Sanchez et al., 2005), and
most

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J.M. Halperin, D.M. Healey / Neuroscience
aintain good adherence for fewer than two months (Perwien et
l., 2004). It has been estimated that fewer than 10% of children
ith ADHD persist with long-term medication treatment (Weiss
et

l., 2006). For behavioral interventions to be implemented over
the
ong-term, willingness among key adults (teachers and parents)
to
mplement highly intensive interventions over long periods of
time,
ith high levels of fidelity, is necessary but extremely
challenging
Chronis et al., 2001; Witt, 1986).
Lastly, the collective evidence suggests that stimulant medi-
ation use has few, if any, long-term benefits for children with
DHD (Charles and Schain, 1981; Loe and Feldman, 2007;
Molina
t al., 2007; Paternite et al., 1999; Satterfield et al., 1982;
Swanson
t al., 2007b; Weiss and Hechtman, 1993; Molina et al., 2009).
his finding also applies to behavioral interventions (Molina et
l., 2007; Pelham and Fabiano, 2008). Thus, although acute ben-
fits of stimulant medication and behavioral interventions are
ell-documented in the literature, the lack of normalization of
unctioning for many children following treatment, lack of
gener-
lization of treatment effects, difficulties in long-term
adherence,
nd lack of clear improvement in long-term functioning
following
he use of these interventions are discouraging.
Given these findings, an alternative approach to ameliorating
he difficulties of children with ADHD is indicted. The extensive
lit-
rature indicating neurobiological deficits in ADHD, and the

strong
ody of evidence regarding neural rehabilitation, suggests that
esearch examining the impact of pharmacologic as well as envi-
onmental manipulations on neural growth, and lasting cognitive
unction, could provide a potentially fruitful avenue for novel
treat-
ent approaches for this highly impairing and oftentimes life-
long
isorder.
. Neural substrates of ADHD
.1. Neuroimaging evidence
Neuroimaging studies suggest an important role for frontostri-
tal circuits along with a wide array of other cortical and
subcortical
rain regions in the pathophysiology of ADHD. Numerous struc-
ural MRI studies have reported smaller regions in the prefrontal
ortex (PFC) of youth with ADHD (Castellanos et al., 1996,
2002;
urston et al., 2004; Filipek et al., 1997; Hynd et al., 1990; Kates
t al., 2002; Mostofsky et al., 2002; Sowell et al., 2003). Simi-
arly, studies have found reduced caudate nucleus size (Filipek
et
l., 1997; Hynd et al., 1993), reduced volume in the globus pal-
idus (Castellanos et al., 1996); and reduced callosal area in
ADHD
Baumgardner et al., 1996; Giedd et al., 1994; Hynd et al., 1991;
Hill
t al., 2003; Lyoo et al., 1996; Semrud-Clikeman et al., 1994).
Structural anomalies have also been reported in several brain
egions outside of the frontal lobes and striatum. Studies have
oted reduced volume in several parts of the cerebellum in

ADHD
Castellanos et al., 2002, 1996; Durston et al., 2004; Posner and
etersen, 1990) as well as posterior cortical anomalies (Durston
et
l., 2004; Filipek et al., 1997; Sowell et al., 2003). Additionally,
sev-
ral (Berquin et al., 1998; Castellanos et al., 1996, 2002;
Castellanos,
001; Hill et al., 2003), but not all (Durston et al., 2004; Filipek
et
l., 1997; Hynd et al., 1990; Mostofsky et al., 2002), studies
report
educed whole brain size in children with ADHD. A key review
of
he structural MRI literature (Seidman et al., 2005) found
support
or the notion that ADHD is associated with frontostriatal abnor-
alities. However, an increasing number of studies,
demonstrating
idespread neural abnormalities affecting other cortical regions
nd the cerebellum, were highlighted.
Recently, studies have begun to use MRI to examine differ-
nces in cortical thickness between individuals with and without
DHD. In a series of developmentally-sensitive longitudinal
stud-
iobehavioral Reviews 35 (2011) 621–634 623
ies Shaw et al. (2006, 2007a,b) reported that children with
ADHD
followed a similar sequential pattern of cortical development,
yet

were delayed by as much as 2–3 years, depending upon the
specific
cortical region (Shaw et al., 2007a). These findings were inter-
preted as suggesting developmental delays rather than
permanent
anomalies in ADHD. Additional research from this group (Shaw
et
al., 2006) found links between cortical thickness and clinical
out-
come such that ADHD children with worse outcome had “fixed”
thinning of the left medial PFC and those with better outcomes
had right parietal normalization, which was suggested to
represent
compensatory cortical change. Similarly, McAlonan et al.
(2009)
reported an association between the magnitude of age-related
growth in the anterior cingulate, striatum and medial temporal
cortex, and improvements in neurocognitive measures of
response
inhibition in school-age children with ADHD. Further, better
clin-
ical outcome was linked to the presence of the DRD4-7-repeat
allele (Shaw et al., 2007b), which was associated with thinner
right orbitofrontal/inferior prefrontal and posterior parietal cor-
tices. This thinning was most apparent in childhood and largely
resolved in adolescence. Yet, these executive function networks
which include the dorsolateral PFC, anterior cingulate cortex
and
inferior parietal lobe remain thinner in adults with ADHD
(Makris
et al., 2007). A recent review focusing on trajectories of brain
devel-
opment (Shaw et al., 2010) suggests that remission in ADHD
with
age may be linked to a normalization of initial delays or deficits
in

brain networks, whereas ADHD persistence into adolescence
may
be associated with a lack of normalization and perhaps a more
deviant trajectory of brain growth.
Consistent with the structural MRI data, functional MRI (fMRI)
studies provide compelling data indicating functional brain dif-
ferences between individuals with ADHD and controls. These
functional studies have linked neural anomalies to deficiencies
in
a wide array of neurocognitive processes including inhibitory
con-
trol (Casey et al., 1997; Durston et al., 2003; Schulz et al.,
2004,
2005a; Tamm et al., 2004; Vaidya et al., 1998), conflict resolu-
tion (Bush et al., 1999; Tamm et al., 2004), motor control
(Rubia
et al., 1999), timing (Durston et al., 2007), attention (Cao et al.,
2008; Sonuga-Barke and Castellanos, 2007; Uddin et al., 2008)
and
working memory (Valera et al., 2005). Although findings have
not
always been consistent, many studies have reported greater and
more diffuse PFC and basal ganglia responses to increased
cognitive
demands in participants with ADHD. These findings are
reminis-
cent of the immature brain function associated with
susceptibility
to interference in young children (Casey et al., 2002).
Recently, there has been an emerging shift in focus among neu-
roimaging studies from the examination of specific brain
regions
to an examination of networks or how specific regions are con-
nected or interact (for review, see Konrad and Eickhoff, 2010).

This
research, which is in its infancy and still has many methodolog-
ical issues to tackle, has focused largely, on two proposed
neural
systems. The Default Mode Network (DFN), comprised of the
pre-
cuneus, posterior cingulate cortex, the medial prefrontal cortex
and portions of the parietal cortex (Schilbach et al., 2008), has
been shown to be most active during rest, and to deactivate dur-
ing active task engagement (Raichle et al., 2001). In contrast, a
second network, which includes the dorsolateral prefrontal cor-
tex, the intraparietal sulcus, and the supplementary motor area
is more quiescent during rest and becomes activated during
peri-
ods of task engagement. This latter system seems more linked to
increased alertness and response preparation than to any
specific
cognitive process (Konrad and Eickhoff, 2010). These two
neural
systems have been described as being ‘anti-correlated’ such that
when the task positive fronto-parietal system turns-on in
response
to task demands, the DFN becomes less active, and when at rest
the DFN is most active, and activity in the fronto-parietal
system
diminishes (Fox et al., 2005; Sonuga-Barke and Castellanos,
2007).
6 and B
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When comparing ADHD participants to controls, some data sug-
est, reduced functional activity and connectivity within the DFN
uring rest (Castellanos et al., 2008; Uddin et al., 2008), but oth-
rs report an overactive DFN during quiescence (Tian et al.,
2006,
008). Sonuga-Barke and Castellanos (2007) have hypothesized
hat, among those with ADHD, the DFN does not adequately
deac-

ivate during task engagement, which in turn interferes with the
eural circuits that underlie the ability to efficiently engage in
ctive tasks. According to this hypothesis, the interference
results
n frequent errors and lapses in attention, as evidenced by the
requently reported increased reaction time variability associated
ith ADHD (Castellanos and Tannock, 2002; Kuntsi et al., 2001;
ussell et al., 2006). Notably, psychostimulant treatment has
been
ound to improve DFN suppression during active task
engagement
n youth with ADHD (Peterson et al., 2009), as well as to
decrease
eaction time variability (Spencer et al., 2009).
Finally, recent research has also examined connectivity during
ctive task engagement. Two studies (Rubia et al., 2010; Vloet et
l., 2010) have reported decreased functional connectivity during
ctive task performance in children with ADHD, which may in
part
e normalized by treatment with stimulant medication (Rubia et
l., 2009). However, in adults with ADHD a more complicated
pat-
ern of regional increases and decreases in functional
connectivity
as been reported (Wolf et al., 2009).
Thus, when taken together, neuroimaging studies provide com-
elling evidence for neurodevelopmental differences between
ndividuals with and without ADHD that span a wide array of
brain
egions and neural circuits.
.2. Neuropsychological findings

Consistent with the neuroimaging data, neuropsychological
ata provide clear evidence that children with ADHD have
impair-
ents in a wide array of neurocognitive domains. Much of the
iterature has focused on executive functions (EFs) which are
ediated by circuits involving the PFC. Studies most consistently
nd deficits in inhibitory control (Barkley, 1997; Casey et al.,
997; Durston et al., 2003), regulation of attention (Pennington
t al., 1993; Swanson et al., 1991; Halperin et al., 1993; Johnson
t al., 2008), working memory (Castellanos and Tannock, 2002;
artinussen et al., 2005), planning (Pennington et al., 1993;
arkley, 1997), and shifting sets (Seidman et al., 1995; Reader et
al.,
994; Hall et al., 1997). Willcutt et al.’s (2005) meta-analysis of
83
tudies (total N = 6703), which focused on 13 EF tasks, clearly
indi-
ated that groups of children with ADHD perform more poorly
than
ontrols on many EF measures, with effect sizes generally in the
edium range (.46–.69). However, because of the relatively mod-
st effect sizes, it was concluded that EF weaknesses are “neither
ecessary nor sufficient to cause all cases of ADHD.”
Nonetheless,
F deficits likely contribute to the functional impairments that
are
resent in many children with ADHD.
Despite the common conceptualization of ADHD as a disorder
of
Fs (Barkley, 1997), children with ADHD also differ from

controls
n a broad range of non-EF functions such as motor coordination
Blondis, 1999; Carte et al., 1996; Kadesjo et al., 2001;
Sheppard
t al., 2000; Steger et al., 2001), perception (Garcia-Sanchez et
al.,
997; Mangeot et al., 2001), language (Beitchman et al., 1987,
1996;
arte et al., 1996; Humphries et al., 1994; Purvis and Tannock,
1997;
irosh and Cohen, 1998), visuomotor integration (Raggio, 1999),
nd learning and memory (Felton et al., 1987). Meta-analyses
Frazier et al., 2004; van Mourik et al., 2005; Willcutt et al.,
2005)
ndicate an array of executive and non-executive function
deficits
n children with ADHD. Furthermore, increased variability in
reac-
ion time (RT) is among the most consistently reported deficits
n children with ADHD (Castellanos and Tannock, 2002; Russell
t al., 2006; Spencer et al., 2009). Additional presumably “non-
executive” parameters have been shown to differentiate ADHD
from non-ADHD children, including signal detectability (d′) and
response bias (ln ˇ) variables from continuous performance tests
(CPTs; Losier et al., 1996). Kuntsi et al. (2001) reported that
RT
variability discriminated ADHD children from controls better
than
measures of inhibition and working memory, and Epstein and
col-
leagues (Epstein et al., 2003) reported variability in d′, ln ˇ, and
RT

to be strongly associated across multiple ADHD symptom
domains.
A recent study (Rommelse et al., 2007) reported that after
control-
ling for “lower order” cognitive processes, there was little
evidence
for primary EF deficits in children with ADHD. As such, some
have
hypothesized that the EF deficits frequently observed in
children
with ADHD may be due to deficiencies in largely subcortical,
reg-
ulatory systems, rather than cortical EF circuitry per se
(Douglas,
1999; Halperin and Schulz, 2006; Rommelse et al., 2007;
Sergeant
et al., 1999; Sonuga-Barke and Castellanos, 2007).
In addition to more broadly assessing neurocognitive deficits in
individuals with ADHD, several studies have focused more
specif-
ically on the three distributed neural networks hypothesized to
underlie attention (Posner and Petersen, 1990). One meta-
analysis
of 14 studies found little or no evidence for any visuospatial
atten-
tion deficits in ADHD, including those functions typically
attributed
to the anterior or executive attention system (Huang-Pollock
and
Nigg, 2003). However, more recently, Johnson et al. (2008)
reported
evidence for impaired alerting and conflict resolution in youth
with
ADHD as measured using the Attention Network Test (ANT;
Fan et

al., 2002). As posited by Posner and colleagues, the alerting
network
is driven largely by noradrenergic input from the locus
coeruleus to
the frontal and parietal lobes, whereas conflict resolution is
more
closely linked to a dopaminergic system involving the basal
ganglia,
anterior cingulate and the dorsolateral prefrontal cortex (Posner
and Petersen, 1990; Posner et al., 2006).
From this review of the neuropsychological and neuroimaging
studies, we can see that the findings do not support simple mod-
els that posit that ADHD is due to dysfunction of a few isolated
brain regions, including the PFC. Rather, individuals with
ADHD dif-
fer from controls on measures derived from multiple brain
regions
and across a wide array of neurocognitive domains. Further,
stud-
ies examining cortical thickness provide compelling evidence
that
careful examination of developmental trajectories may be key to
uncovering the neural substrates of ADHD, and that clinical
out-
comes might be linked to cortical development and
compensatory
mechanisms that develop throughout childhood.
4. Neurodevelopmental perspectives on ADHD
Throughout the past decade investigators have increasingly
embraced the notion that ADHD is best viewed within the con-
text of a developmental trajectory rather than that of a static
medical condition (Halperin and Schulz, 2006; Sagvolden et al.,
2005; Sonuga-Barke and Halperin, 2009; Taylor, 1999), and

notably,
that this developmental trajectory may be different for boys and
girls (Mahone and Wodka, 2008). It is through this developmen-
tal perspective that one is most likely to identify the mediators
and moderators of the diverse outcomes characteristic of youth
with ADHD. Compelling data support an array of hypotheses
that posit executive (Pennington and Ozonoff, 1996), motiva-
tional (Sonuga-Barke et al., 1992, 1996), inhibitory control
(Barkley,
1997), cognitive-energetic (Sergeant et al., 1999) and reward-
related (Sagvolden et al., 2005) deficits as being central to the
etiology of ADHD. More recently, it has been proposed that
there
may be multiple causal pathways that contribute to the
emergence
of ADHD in early childhood (Nigg et al., 2005; Sonuga-Barke,
2002).
However, little consideration has been afforded to pathways out
of ADHD or the mechanisms by which the severity of the
disorder
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iminishes over time, which could serve as the conceptual basis
for
he development of novel, perhaps enduring, interventions.
In an attempt to explain the commonly observed diminu-
ion in ADHD symptoms across the lifespan, Halperin and
Schulz
2006), Halperin et al. (2008a) have posited distinct
neurocognitive
echanisms for the etiology of and relative recovery from ADHD.
pecifically, they hypothesized that ADHD is caused by non-
cortical

eural dysfunction that is present early in ontogeny and remains
elatively static throughout life. In this regard, the disorder is
never
eally “cured” or “out-grown.” Nonetheless, they posited that the
eduction of symptoms oftentimes seen over development are at
east partially accounted for by the degree to which prefrontally-
ediated EFs and other higher cortical functions, which emerge
hroughout childhood and adolescence, can compensate for these
ore primary and enduring subcortical deficits.
As such, the PFC and other cortical regions may be involved in
he reduction in severity of symptoms and impairment from
ADHD
ather than the etiology of the disorder. The efficiency of later
cor-
ical developmental processes may determine the extent to which
n individual can compensate for or have a diminution of ADHD
ymptoms. This hypothesis is consistent with data suggesting
that
rajectories of cortical development throughout middle childhood
ight be closely linked to ADHD outcomes (Shaw et al., 2006,
007b), and that lower reaction time variability is associated
with
ncreased activation of the prefrontal circuit (prefrontal cortex
and
audate) in children with ADHD (Suskauer et al., 2008). This
latter
nding was interpreted as prefrontal compensation for a more
pri-
ary motor deficiency. In addition, fMRI findings indicate that
PFC

ctivation in response to inhibition in adolescents with childhood
DHD corresponds to the persistence of symptoms, such that
those
ho are less symptomatic appear more like never-ADHD controls
Schulz et al., 2005a,b). Finally, longitudinal data (Halperin et
al.,
008b) examining neurocognitive functioning in adults who had
DHD in childhood, indicate that only those in whom ADHD has
ersisted differ from controls on measures of EFs such as
working
emory, sustained attention and inhibitory control.
This hypothesized developmentally-related compensation for
arlier deficits may also be related to changes in functional
onnectivity in the brain that occurs over development. For
exam-
le, Dosenbach et al. (2007) described two distinct “top-down”
unctional control networks in adults; a frontoparietal network
nvolved in adaptive online task control and a cinguloopercular
etwork involved in more stable set control. When compared
cross groups of children, adolescents and adults, a clear pat-
ern of developmentally-related differences emerged such that
hort-range connections associated with these networks
decreased
hereas long-range connections increased across development
Fair et al., 2007). These findings suggest greater segregation
etween networks as well as improved integration within net-
orks with increased age. It is possible that these developmental
rocesses that support the maturation of this dual-network con-
rol system play a critical role in the hypothesized compensation
or earlier deficits in youth with ADHD. Notably, this pattern of
evelopmental decreases in local connectivity and increases in
ong-range connectivity does not appear limited to these specific

eural systems. Rather, it appears to be a more general develop-
ental principle that operates throughout the brain (Fair et al.,
009; Supekar et al., 2009).
Finally, it has been hypothesized that the persistence of ADHD
nto adulthood, or the relative diminution of symptoms across
the
ifespan, is genetically mediated (Faraone, 2004). Supporting
this
ypothesis, Kuntsi et al. (2005) found in a longitudinal study of
000 twin pairs that symptom stability in ADHD from early-to-
id childhood was primarily due to shared genetic influences.
urther, at the molecular level, children with ADHD who had at
east one copy of the DRD4 7-repeat allele had a distinct trajec-
iobehavioral Reviews 35 (2011) 621–634 625
tory of cortical development characterized by normalized
cortical
thinning, were less likely to maintain a diagnosis of ADHD at
follow-
up, had higher IQs, and exhibited better global functioning than
children with ADHD without a DRD4 7-repeat allele; despite no
differences in ADHD symptoms at baseline. This is consistent
with
findings of Swanson et al. (Swanson et al., 2000) who reported
that
the presence of the DRD4 7-repeat allele in children with
ADHD
was associated with fewer neuropsychological deficits.
Addition-
ally, the presence/absence of the 7-repeat allele may also
underlie

a key gene × environment interaction in relation to ADHD, its
tra-
jectory, and potential treatment response. As compared to
children
without the DRD4 7-repeat allele, the behavior of those with a
7-repeat allele was found to be more sensitive to the quality of
par-
enting received (Sheese et al., 2007) and to have a better
treatment
response to a psychosocial parenting intervention (Bakermans-
Kranenburg et al., 2008). These data suggest that there may be
genetic differences in the degree to which both the environment
and delivered treatments influence developmental trajectories in
children.
Thus, from a developmental perspective, interventions that pro-
mote neural growth, functional connectivity, and lasting
cognitive
facilitation could potentially provide a fruitful avenue for novel
treatment approaches for this highly impairing oftentimes life-
long
disorder. Further, it is possible, that some children may be more
or less genetically susceptible to respond to such interventions.
As described below, accumulating evidence clearly indicates
that
early brain development is highly susceptible to environmental
influences.
5. Neural development
Human central nervous system development proceeds in a sys-
tematic manner that begins before conception and continues at
least into early adulthood. Nevertheless, brain development is
non-linear, and progresses in a localized and region-specific
man-
ner that coincides with functional maturation (Huttenlocher and

Dabholkar, 1997; Keshavan et al., 1994). Of particular
relevance
are the relatively late and protracted development of the basal
ganglia and cerebral cortex, including the PFC, and the motor
and
higher cortical functions that they mediate (Asato et al., 2010;
Barkovich, 2005; Barnea-Goraly et al., 2005; Benes, 1989;
Chelune
et al., 1986; Giedd et al., 1996, 1999; Gogtay et al., 2004;
Goldman,
1971; Goldman and Galkin, 1978; Huttenlocher and de Court,
1987;
Huttenlocher, 1990; Huttenlocher and Dabholkar, 1997; Hynd et
al., 1993; Keshavan et al., 1994; McKay et al., 1994; Mrzljak et
al.,
1990; Passler et al., 1985; Welsh et al., 1991; Yakovlev and
Lecours,
1967).
The human brain develops primarily in utero and is approxi-
mately 80% of adult size by the age of 2 years (Giedd et al.,
1999;
Kretschmann et al., 1986). The process of myelination also
begins
in utero and proceeds rapidly up to age 2 years (Brody et al.,
1987;
Kinney et al., 1988), but continues well into adolescence and
early
adulthood (Asato et al., 2010). The first two years of life are
also a
period of rapid synapse formation that occurs at varying times
and
rates in different brain regions, reaching maximum density at
age 3
months in the auditory cortex and at age 15 months in the PFC,
and

resulting in an overproduction of synapses (Huttenlocher and de
Court, 1987; Huttenlocher and Dabholkar, 1997).
Synaptogenesis
is followed by a plateau phase extending over several years,
during
which neurons begin to form complex dendritic trees (Mrzljak
et
al., 1990). These two processes seem to account for the increase
in
cortical gray matter in childhood found in MRI studies (Giedd
et al.,
1996, 1999; Gogtay et al., 2004; Shaw et al., 2006, 2007a).
Subsequent brain development, beginning at about 5 years of
age, is marked as much by cortical organization and refinement
6 and B
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s by neuronal growth. Cortical gray matter continues to thicken
uring the school-age years with about half of the cortical
regions
ttaining peak thickness by the median age of 7.5 years (Shaw
t al., 2007a), and cortical thickness peaking at around 10.2–12.8
ears in the parietal cortex and around 11.0–12.1 years in the
rontal lobe (Giedd et al., 1996, 1999; Gogtay et al., 2004).
Through-
ut this period the experience-dependent pruning of inefficient
ynapses in the cortex is also taking place (Giedd et al., 1996,
1999;
ogtay et al., 2004; Huttenlocher and de Court, 1987;
Huttenlocher
nd Dabholkar, 1997). Synaptic pruning progresses in a region-
pecific manner and eventually reduces synaptic density to 60%
f maximum (Huttenlocher and de Court, 1987; Huttenlocher
nd Dabholkar, 1997), although it is mostly after puberty that
he developmental process of cortical thinning occurs. These
pro-

esses all facilitate efficient neural transmission and are
necessary
or the functional maturation of the brain. As in humans, this
lateau phase of cortical thickness lasts through puberty in mon-
eys and is thought to be indicative of the need for consistent
nd high synaptic density during the formative years when learn-
ng and experiences are most intense (Bourgeois et al., 1994).
hile much of neural development is likely to be genetically
rogrammed, considerable data indicate that variability in devel-
pment across the lifespan is at least in part related to one’s
xperiences.
.1. Environmental influences on neurodevelopment
The notion that life experiences and one’s environment can
nfluence both cognitive and neural development was first
clearly
rticulated in 1949 by Hebb (Hebb, 1949), who postulated that
ynapses are strengthened when presynaptic fibers repeatedly
articipate in activating the postsynaptic neuron. Subsequently,
ubel and Wiesel’s (Hubel and Wiesel, 1970; Wiesel and Hubel,
965) classic experiments demonstrated environmental influences
n neural plasticity and neurodevelopment in the visual system
n cats. In the 1960s Rosensweig and colleagues (Rosenzweig
t al., 1962, 1968; Rosenzweig, 1966; Rosenzweig and Bennett,
969) systematically began to examine the influence of environ-
ental manipulations on brain weight, cortical thickness and the
tructure of dendrites, as well as on cognitive functioning. In the
970 s, Greenough et al. (1973, 1978), Greenough and Volkmar
1973) expanded this work to include the effects of
environmental
nhancements on a range of parameters of brain function
including
endritic branching, spine density, synaptogenesis, angiogenesis,

nd gliogenesis.
Since these early seminal studies, an abundant scientific liter-
ture, primarily, but not exclusively, in non-human species, has
xamined the impact of environmental influences on molecular
nd cellular aspects of neural development as well as on an array
f behavioral and cognitive functions. The precise environmen-
al manipulations required to best facilitate specific aspects of
eural development, the critical periods in development when
hey are optimally applied, and the duration necessary for such
nterventions have remained somewhat elusive. However, there
s no longer doubt that brain development is highly respon-
ive to increased levels of physical activity/exercise as well as
nvironmental enrichment. While a full review of this extensive
iterature is beyond the scope of this paper, a brief overview is
arranted. A plethora of studies in rodents have shown that envi-
onmental enrichment increases neuronal size, dendritic
branching
nd spine number, synaptic density and overall neurotransmis-
ion in the neocortex (Diamond et al., 1964; Diamond, 1967;
lobus et al., 1973; Green and Greenough, 1986; Greenough et
l., 1973, 1978; Greenough and Volkmar, 1973; Leggio et al.,
005; Rosenzweig and Bennett, 1996). Environmental
enrichments
lso enhance neurogenesis (Kempermann et al., 1998; Nilsson et
al., 1999), long-term potentiation (LTP; Duffy et al., 2001),
neu-
rotrophin levels (Ickes et al., 2000; Pham et al., 1999, 2002),
dendritic spine growth and branching (Faherty et al., 2003;
Green
et al., 1983; Rampon et al., 2000), synaptophysin levels (Frick

and Fernandez, 2003; Nithianantharajah et al., 2004), and nerve
growth factor mRNA and CREB gene expression (Torasdotter et
al., 1996, 1998; Williams et al., 2001) in the dentate gyrus of
the
hippocampus. Consistent with the effects of environmental
enrich-
ment on neurodevelopment, numerous studies have shown that
it significantly improves performance on an array of spatial and
nonspatial memory tasks in rats (Leggio et al., 2005; Nilsson et
al., 1999) and mice (Kempermann et al., 1997; Williams et al.,
2001).
Evidence for the impact of environmental enrichment in
humans is far more limited and centers largely on the concept
of ‘cognitive reserve’ as related to the emergence of
Alzheimer’s
disease. Epidemiologic data provide a compelling link between
increased participation in intellectual and social activities in
daily
life and a slower cognitive decline in the elderly (Scarmeas and
Stern, 2004). Furthermore, cognitive exercises show promise as
effective interventions for slowing the trajectory of cognitive
and
functional decline that is associated with dementia (Gates and
Valenzuela, 2010).
In addition to environmental enhancements, physical exercise,
most commonly in the form of wheel running in rodents, has
also
been shown to enhance neural growth and development, as well
as associated behavioral and cognitive functions. Physical exer-
cise increases levels of synaptic proteins (Vaynman et al.,
2006),
glutamate receptors (Farmer et al., 2004) and the availability of
brain-derived neurotrophic factor (BDNF) (Berchtold et al.,
2005)

and insulin-like growth factor-1 (Trejo et al., 2001), all of
which can
enhance neural plasticity. Specifically, treadmill exercises in
rats
increase cell proliferation via insulin-like growth factor-1
(Trejo
et al., 2001) and cell proliferation and survival in the dentate
gyrus
(van Praag et al., 1999b). These neural changes in response to
phys-
ical exercise are accompanied by behavioral changes such that
physical exercise enhances spatial learning (Fordyce and Farrar,
1991; Fordyce and Wehner, 1993) and passive avoidance
memory
(Samorajski et al., 1985).
Physical exercise has also been reported to increase BDNF lev-
els (Ferris et al., 2007; Gold et al., 2003; Rasmussen et al.,
2009;
Seifert et al., 2010; Strohle et al., 2010; Tang et al., 2008;
Zoladz
et al., 2008), enhance cognitive performance (Baker et al.,
2010;
Ferris et al., 2007; Laurin et al., 2001), and to promote brain
health
(Colcombe et al., 2003) in human adults. One study that
examined
BDNF levels in children with ADHD reported that as compared
to
controls, those with ADHD had increased levels of BDNF, and
that
BDNF levels correlated with neuropsychological performance as
indicated by number of errors on a continuous performance test
(Shim et al., 2008). However, BDNF change in response to
exercise
was not assessed.

Exercise and environmental enhancements also have been
shown to have a positive effect on outcome in several animal
mod-
els of neurodevelopmental disorders. Wheel running from an
early
age delays the onset of motor deficits in knock-out mouse mod-
els of Huntington’s disease (van Dellen et al., 2008), and
complex
motor training in rats enhances synaptogenesis in the motor cor-
tex and motor coordination following lesions of the
sensorimotor
cortex (Jones et al., 1999). Perhaps more relevant to the current
thesis, chronic exercise blunted the developmental rise of blood
pressure while increasing glutamic acid decarboxylase mRNA in
the
caudal hypothalamus in spontaneously hypertensive rats, which
are commonly used as an animal model for ADHD (Little et al.,
2001). Finally, rat pups born from alcohol-intoxicated mothers
have
been shown to have diminished c-Fos activity in the
hippocampus
along with an array of neurobehavioral deficits. Postnatal tread-
and B
m
(
t
w
a
m

b
e
e
c
u
o
b
t
i
b
i
(
n
1
d
t
f
w
s
i
r
(
a
t
i
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s
a
e
e
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e
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a
e
p
t
e
a
s
o
a
t
e
n
n
t
t
r
c
g
c
l
n
ill exercise not only enhances c-Fos activity in the hippocampus
Sim et al., 2008), but exercise and various forms of
environmen-
al enrichment reduce the severity of behavioral deficits
associated
ith maternal alcohol intoxication (Hannigan et al., 2007). Thus,
t least in animals, interventions involving environmental enrich-
ent and physical exercise can yield lasting positive effects on

ehavioral anomalies due to both genetic and environmental
tiologies.
Most rodent studies focusing on environmental enrichments
mploy a combination of social (e.g., multiple animals in a
cage),
ognitive (e.g., toys, tunnels) and motor (e.g., running wheels)
stim-
li. As such, it is difficult to determine the extent to which any
ne of these contributes to the positive outcome. Data suggest
that
oth exercise and environmental enhancement contribute to posi-
ive neurodevelopmental outcomes, although not necessarily in
an
dentical manner. Some evidence suggests that exercise affects
the
rain similarly to environmental enrichment, with changes
includ-
ng enhanced hippocampal LTP (Kim et al., 2004) and
neurogenesis
van Praag et al., 1999a,b), as well as increased hippocampal and
eocortical neurotrophin mRNA expression (Neeper et al., 1995,
996). Other studies suggest that individual elements may have
ifferent effects on neural plasticity and development. Manipula-
ions involving problem solving and coordination increase
synapse
ormation in the cerebellar cortex (Kleim et al., 1997, 1998),
hereas repetitive physical exercise increases cerebellar blood
ves-
el density (Black et al., 1990). Exercise has also been found to
mprove spatial memory and preserve cognitive function in aging
ats (Anderson et al., 2000; Fordyce and Farrar, 1991) and mice
Anderson et al., 2000; Fordyce and Wehner, 1993; van Praag et
l., 1999a). Nevertheless, some data (Faherty et al., 2003)

suggest
hat environmental enhancements are more effective for facilitat-
ng neural changes than exercise alone, while others (Lambert et
al.,
005) suggest that exercise, but not cognitive stimulation
improves
patial memory.
Thus, preclinical data indicate that environmental enhancement
nd exercise have positive morphological, chemical and
cognitive
ffects on the brain across the lifespan. Much of the human lit-
rature, with moderate success, has focused on the latter end of
he developmental spectrum, with the aim of addressing cogni-
ive declines characteristic of the elderly. These data suggest
that
ognitive and social stimulation, as well as physical exercise,
may
elay the onset of dementia and other neurodegenerative dis-
ases (Scarmeas and Stern, 2004; Ravaglia et al., 2008; Kramer
and
rickson, 2007; Arkin, 2007; Rolland et al., 2007). Furthermore,
in
dults, cognitive training increases cortical brain activity (Olesen
t al., 2004) and alters dopamine D1 receptor binding in both the
refrontal and parietal cortices (McNab et al., 2009).
Although early childhood is the period of greatest neural plas-
icity, and the most prominent cell proliferation in response to
xercise has been shown to occur at younger ages in rats (Kim et
l., 2004), far less research has been conducted with children.
Two
tudies have examined exercise regimes in children with ADHD;
ne reporting a blunted catecholamine response (Wigal et al.,
2003)
nd the other showing changes in eye blink responses and reduc-

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