Content uploaded by Alan L. Smith
Author content
All content in this area was uploaded by Alan L. Smith
Content may be subject to copyright.
Journal of Attention Disorders
17(1) 70 –82
© 2013 SAGE Publications
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1087054711417395
http://jad.sagepub.com
Research Brief
417395JAD17110.1177/108705471141739
5Smith et al.Journal of Attention Disorders
© 2013 SAGE Publications
Reprints and permission:
sagepub.com/journalsPermissions.nav
1Purdue University, West Lafayette, IN, USA
2University of Vermont, Burlington, USA
Corresponding Author:
Alan L. Smith, Department of Health and Kinesiology, 800 West Stadium
Avenue, Purdue University, West Lafayette, IN 47907-2046, USA
Email: alsmith7@purdue.edu
Pilot Physical Activity Intervention
Reduces Severity of ADHD Symptoms
in Young Children
Alan L. Smith1, Betsy Hoza2, Kate Linnea2, Julia D. McQuade2, Meghan Tomb2,
Aaron J. Vaughn2, Erin K. Shoulberg2, and Holly Hook1
Abstract
Objective: Physical activity associates with mental health and neurocognitive function, showing potential for addressing
ADHD symptoms. As a preliminary assessment of this potential, the authors piloted a before-school physical activity
intervention for young children. Method: Seventeen children (Grades K-3) exhibiting four or more hyperactivity/
impulsivity symptoms on the Disruptive Behavior Disorders Rating Scale (Pelham, 2002) completed about 26 min of
continuous moderate-to-vigorous physical activity daily over eight school weeks. The authors administered cognitive,
motor, social, and behavioral functioning measures at pre- and postprogram, assessed response inhibition weekly, and
coded negative behaviors daily. Results: Several measures showed significant or marginally significant change over time
(effect size = 0.35-0.96) with additional measures showing meaningful effect size values (≥ 0.20). Response inhibition effects
were most consistent. Most participants (64% to 71%) exhibited overall improvement according to postprogram parent,
teacher, and program staff ratings. Conclusion: Physical activity shows promise for addressing ADHD symptoms in young
children. ( J. of Att. Dis. 2013; 17(1) 70-82)
Keywords
ADHD treatment, attention, exercise, school-based intervention
ADHD is a chronic health condition that is considered the
most common neurobehavioral disorder experienced by
children (American Academy of Pediatrics, 2000). Present
in 3% to 7% of the school-aged population, ADHD is
expressed in inattentive, hyperactive/impulsive, and com-
bined forms (American Psychiatric Association, 2000). The
key features of inattention and/or hyperactivity/impulsivity
are generally present by the age of 7 and foster impairments
across at least two settings, most often home and school.
Such impairments include academic and social difficulties,
family distress and dysfunction, and challenges in various
extracurricular settings such as sports (Hoza, Owens, & Pel-
ham, 1999). Difficulties often persist and expand (e.g., driv-
ing accidents, illicit drug use, school dropout) over the
adolescent and adult years (Mannuzza & Klein, 2000), mak-
ing chronic management of the disorder essential. In light of
the chronic nature of ADHD and the substantial economic
impact of ADHD in children and adolescents, estimated to
be US$36 to US$52.4 billion annually (Pelham, Foster, &
Robb, 2007), it is recognized as a major public health issue
(National Institutes of Health, 2000).
The primary treatment approaches for ADHD are medi-
cation management, behavioral treatment, or a combination
of the two. The National Institute of Mental Health
Multimodal Treatment Study of Children with ADHD
(MTA; MTA Cooperative Group, 1999a, 1999b; Richters
et al., 1995) significantly extended the database on long-term
treatment and serves as a benchmark for research quality
(Schachar et al., 2002). The MTA findings suggest that a
rigorously monitored medication management plan offers
meaningful benefits to school-aged children, whether alone
or in combination with behavioral treatment. Medication
and combined treatments show an advantage over behav-
ioral and community care for ADHD outcomes. However,
when extending assessment beyond ADHD symptoms to
other functional outcomes (e.g., academic performance,
aggression, internalizing, social skills/relationships), com-
bined approaches may be preferred (MTA Cooperative
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
Smith et al. 71
Group, 1999a). Consistent with this conclusion, secondary
analyses involving a composite outcome variable demon-
strated a small benefit of combined treatment over medica-
tion management and moderate to moderately large benefit
over behavioral treatment and community care (Conners et al.,
2001). Anchoring these outcomes to clinical relevance,
additional secondary analyses indicated that 68% of those
receiving combined treatment achieved parent and teacher
average symptom ratings of “just a little” or below, consid-
ered a threshold for successful treatment, followed by medi-
cation management (56%), behavioral treatment (34%), and
community care (25%; J. M. Swanson et al., 2001).
Despite these documented benefits, MTA follow-up
assessments show reduction of effects by 10 months post-
treatment (MTA Cooperative Group, 2004) and no detect-
able treatment group differences 22 months after completion
of study treatment (Jensen et al., 2007). Moreover, there are
a variety of challenges associated with ADHD treatment. As
evident in the MTA findings, there is substantial discrep-
ancy between what is offered to children in community care
settings and what yields greatest success. Demands associ-
ated with most successful treatment such as frequent visits
to the physician and/or therapist, taking medicine one or
more times daily, and chronic behavioral management are
onerous. Moreover, parents may be hesitant to pursue stim-
ulant medication management, especially in young children,
because of tolerability (T. Wigal et al., 2006), documented
side effects such as trouble in sleeping and reduced growth
rate (J. Swanson et al., 2006; T. Wigal et al., 2006), and extant
debate on cardiovascular risk (Biederman, Spencer, Wilens,
Prince, & Faraone, 2006; Nissen, 2006). Thus, additional
intervention options are needed that are amenable to long-
term maintenance and more easily incorporated into the life-
styles of children. In this pilot work, we explore the potential
for physical activity to serve as such an option.
Physical activity is any movement produced by skeletal
muscles and resulting in energy expenditure (Caspersen,
Powell, & Christenson, 1985). For children this can come in
the form of active transport (e.g., walking or biking to
school), informal play, sports, physical education, and delib-
erately structured activity to promote fitness components
(i.e., exercise). Though the bulk of work examining physical
activity and health has focused on adults, emerging evi-
dence shows an association between physical activity and a
host of physical and mental health markers in young people
(Stensel, Gorely, & Biddle, 2008). The indicators of mental
health that are most extensively examined in the youth phys-
ical activity literature are self-concept, depression, and anxi-
ety. The extant literature suggests that physical activity
involvement positively associates with various components
of self-concept and negatively associates with anxiety and
depression (for reviews, see Calfas & Taylor, 1994; Strong
et al., 2005). Specifically examining children aged 6 to 14
years with ADHD, a recent study by Kiluk, Weden, and
Culotta (2009) showed greater sport involvement to be asso-
ciated with lower anxious-depressed, internalizing prob-
lems, and affective T-scores on the Child Behavior Checklist
(Achenbach, 1991). Such associations were not observed
for a comparison sample of children with learning disabili-
ties, suggesting that youth at risk for mood problems may
particularly benefit from physical activity. Moreover, for
both boys and girls with ADHD, those engaging in three or
more sports showed significantly lower anxious-depressed
T-scores than those engaging in two or fewer sports. Thus,
consistent involvement in physical activity may be an impor-
tant matter when considering physical activity as a strategy
for addressing ADHD symptoms.
Beyond the link to mental health described above, physi-
cal activity also shows associations with neurocognitive
function. Early interest in this connection was largely
focused on later adulthood, where physically active life-
styles and aerobic physical activity interventions have been
linked to the preservation and enhancement of cognitive
function, particularly executive control processes (Hall,
Smith, & Keele, 2001; Kramer et al., 1999). Extant animal
and human work suggests that such physical activity affects
the brain in ways that would be expected to impact executive
function. For example, animal studies suggest that, in
rodents, aerobic physical activity benefits learning and
memory, plasticity in the hippocampal formation, neurogen-
esis, and neurotrophin expression (see Cotman, Berchtold, &
Christie, 2007; Olson, Eadie, Ernst, & Christie, 2006). Also,
recent human studies suggest that aerobic physical activity
enhances frontal brain function (Colcombe et al., 2004),
affects frontal brain structure (Colcombe et al., 2006),
increases serum levels of brain-derived neurotrophic factor
(BDNF; Ferris, Williams, & Shen, 2007; Tang, Chu, Hui,
Helmeste, & Law, 2008), and promotes hippocampal neu-
rogenesis (Pereira et al., 2007). Such findings are relevant
to ADHD in that executive control deficits, in particular
behavioral inhibition challenges, are core impairments asso-
ciated with ADHD (Barkley, 1997).
Importantly, ADHD symptoms extend beyond executive
function deficits. Indeed, it appears that multiple brain
regions and neurochemical pathways are implicated in the
disorder (see Nigg, 2006). Compared with typically devel-
oping individuals, those with ADHD have been shown to
have less brain volume not only in frontal regions associ-
ated with executive function but also in the cerebellum and
other cortical regions (Halperin & Healey, 2011; Seidman,
Valera, & Makris, 2005). Moreover, despite children with
hyperactive/impulsive and combined-type ADHD ostensi-
bly being quite active as a function of hyperactivity, chil-
dren with ADHD are at risk for movement skill deficits and
motor problems as well as poor levels of physical fitness
(Diamond, 2000; Harvey & Reid, 2003; Nigg, 2006).
Patterns of chronic physical activity engagement of chil-
dren with ADHD have not been examined, though the
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
72 Journal of Attention Disorders 17(1)
documented movement skill and fitness deficits suggest
that hyperactivity in itself does not yield movement and fit-
ness outcomes consistent with typically developing chil-
dren. Thus, exposing children with ADHD to consistent,
deliberately structured physical activity may help accom-
modate for observed motor and fitness deficits associated
with the disorder as well as other cognitive, behavioral, and
social deficits. In light of its broad ranging effects on the
brain, physical activity may be well suited for addressing a
disorder such as ADHD, which is characterized by impre-
cise etiology and broad neurocognitive effects.
More recently, attention has been paid to the potential
link of physical activity with cognitive function and aca-
demic performance in children (see Best, 2010; Centers for
Disease Control and Prevention, 2010; Tomporowski,
Davis, Miller, & Naglieri, 2008). Sibley and Etnier (2003)
conducted a meta-analysis of initial efforts exploring the
activity to cognitive function link, including 44 studies (125
effect sizes) in their analysis. The overall effect size was
0.32, and moderator analyses showed that middle school–
aged children (effect size = 0.48 ± 0.27) followed by young
elementary children (effect size = 0.40 ± 0.26) showed the
largest effect sizes. The authors speculated that findings for
young elementary-aged children may be explained by the
importance of movement to young children’s cognitive
development. This is an interesting consideration in light of
the above-described movement challenges experienced by
many children with ADHD and recent animal work address-
ing physical activity and neurogenesis. Kim et al. (2004)
demonstrated age-related differences in the effect of tread-
mill exercise on cell proliferation within the dentate gyrus of
rats. Specifically, they found treadmill exercise to increase
cell proliferation of 4-, 8-, and 62-week-old rats, with the
most active proliferation in the youngest rats and less active
proliferation with increasing age. This work suggests that
employing physical activity as an intervention to address
ADHD may be especially beneficial during young
childhood.
The association of physical activity with positive men-
tal health and neurocognitive function in children, the rela-
tively broad ranging effects of physical activity, and the
plasticity of the young brain point to physical activity as a
potentially valuable intervention for children with ADHD
symptoms. Aside from descriptive work on physical activ-
ity and cognitive function (Gapin & Etnier, 2010) and
research examining the impact of acute physical activity
bouts (Tantillo, Kesick, Hynd, & Dishman, 2002; S. B.
Wigal et al., 2003) on children with ADHD, we located no
published investigations of an extended aerobic physical
activity protocol for the management of ADHD symp-
toms. An extended protocol presumably would be neces-
sary for effective ADHD management and to afford any
benefits to brain development. The present study was
designed as a preliminary effort to address the viability of
such a protocol. Specifically, the purpose of the present
study was to pilot a before-school physical activity inter-
vention for reducing ADHD symptoms in young children.
We hypothesized that completing physical activity before
each school day over a 9-week time frame would foster
adaptive change in cognitive, behavioral, motor, and social
symptoms associated with ADHD.
Method
Participants
Eligible participants (N = 17) were identified using parent
and teacher ratings on the Diagnostic and Statistical
Manual of Mental Disorders (4th ed.; DSM-IV) version of
the Disruptive Behavior Disorders (DBD) Rating Scale
(Pelham, 2002; Pelham, Gnagy, Greenslade, & Milich,
1992). The DBD is comprised of the DSM-IV symptoms of
ADHD, oppositional defiant disorder (ODD), and conduct
disorder (CD); however, CD items were not used given the
ages of participants. Kindergarten through third graders in
a diverse, low-SES Vermont community (54% of students
on free or reduced lunch) with parental consent were screened.
We sought children at risk for ADHD and set four or more
symptoms of hyperactivity/impulsivity as a requirement for
eligibility. The mean number of symptoms endorsed was
7.1 for hyperactivity/impulsivity (SD = 1.9), 5.6 for inatten-
tion (SD = 2.7), and 4.9 for ODD (SD = 2.6). Participants
were 5.2 to 8.7 years of age at entry (M = 6.7, SD = 1.0).
Three children dropped out in the first 2 weeks and 14
completed the program; data from these 14 children, all
medication naïve, are summarized in the “Results” section.
Six boys and 8 girls comprised the completers. In the final
sample, 12 participants were White and the 2 remaining
participants were Black.
Procedure
The physical activity program was designed to maximize
participant involvement in continuous moderate-to-vigorous
physical activity (MVPA) within an engaging, before-
school setting. Each day the program was for 30 min, with
participants completing four stations within small groups
(about five children) that each lasted 6 min. Stations were
designed to foster sustained MVPA within the context of
games and activities that required participants to employ a
variety of motor skills (e.g., moving objects to different
locations in the activity area, various forms of locomotion—
skipping, running, hopping, crab walking). Staff members
were trained to limit the provision of verbal instructions
with the children being sedentary. Instead, staff members
modeled desired behaviors and provided instruction while
the children were moving. One staff member supervised
each station. In addition to these stations, one large-group
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
Smith et al. 73
“warm-up” aerobic running activity (e.g., tag) was com-
pleted in 1 to 2 min each day following review of program
rules (i.e., keep your hands and feet to yourself, wait your
turn to speak, speak nicely to others, and follow directions
the first time) and prior to breaking into the stations.
Therefore, a total of about 26 min of MVPA was scheduled
on a given program day. The balance of time was used for
transitions (e.g., removing coats, changing activity sta-
tions).
Program staff received approximately 20 hr of training
and practice in the implementation of the program prior to
program initiation to ensure reliable administration of the
intervention and assessment procedures. Most of the staff
who ran the physical activity program had previous experi-
ence working with youth with ADHD or teaching physical
activity skills in camp settings, and all were undergraduate
or graduate students in psychology.
Preassessments listed in the “Pre-Post Program Meas-
ures” section were completed the week immediately before
the start of the program. Eight weeks of physical activity
were then completed over a 9-week period. Two weeks (9
school days) of the intervention were conducted, followed
by the participants’ 1.5-week spring break, a partial (2 days)
week of intervention, and then five full intervention weeks.
In addition to the pre–post measures, various weekly and
daily ecological/behavioral assessments were completed
(described below). Postassessments were completed the
week immediately following the program; teachers, par-
ents, and staff also provided global improvement ratings
(described below) at the end of the program.
Children were provided school bus transportation to the
program. Participants were not compensated monetarily,
but attendance was tracked daily and children were given a
star on a chart for each day they attended. Children with
good attendance received good attendance certificates peri-
odically throughout the program and an attendance award
(a small trophy) on the last day of the program. Rates of
attendance were high, with 86% (12 of 14) of children
meeting or exceeding 75% attendance. A problem arose
partway through the program with children sitting down to
rest as soon as they got tired or out-of-breath; hence, a sys-
tem was initiated whereby children who “kept moving” the
entirety of at least three of the activity stations earned a
small trinket from a grab bag that day. This was contingent
only on movement, not any other behavior(s). To avoid
confounds, no formal behavior management system was in
place and staff were instructed to handle negative behavior
as best they could; they were not permitted to use time out
or other behavioral strategies for negative behavior.
Measures
Given the preliminary nature of this pilot work, and the
absence of prior studies indicating what measures may be
sensitive to physical activity effects on ADHD symptoms,
a broad range of measures was employed. Direct measures
of motor, cognitive, social, and behavioral capacities were
used, as well as behavioral and improvement ratings by
multiple informants. We report results of all measures
described below, emphasizing effect sizes rather than sig-
nificance of comparisons, in the hope that these preliminary
data are useful to future investigators exploring physical
activity effects across a variety of outcome domains.
Pre–Post Program Measures
Bruininks–Oseretsky Test of Motor Proficiency, 2nd edition
(BOT-2). The BOT-2 (Bruininks & Bruininks, 2005) is a
widely used, reliable, valid, and age-normed assessment of
children’s motor proficiency, including both gross-motor
and fine-motor tasks. The short form of the BOT-2 was
administered in the present study. It consists of the follow-
ing fine-motor tasks: drawing lines through crooked paths
on a sheet of paper, folding paper on lines, copying a square,
copying a star, and transferring pennies one at a time from
one hand to the other (and then placing in a box) for 15 s,
and the following gross-motor tasks: synchronized jumping
in place, synchronized tapping of fingers and feet, walking
forward on a line, standing on one leg on a small beam for
10 s (eyes open), hopping on one foot for 15 s, dropping and
catching a tennis ball after one bounce using both hands,
dribbling a tennis ball (alternating hands), knee push-ups
for 30 s, and sit ups for 30 s. The short form of the BOT-2
produces a single score standardized to a mean of 50 and
standard deviation of 10, on which higher scores index
greater motor proficiency. The norm sample for the BOT-2
consisted of randomly selected individuals from ages 4
through 21 years, stratified within age groups by sex, race/
ethnicity, socioeconomic status, and disability status
(including ADHD; Bruininks & Bruininks, 2005). For the
short form of the BOT-2 with knee push-ups, the 5- through
8-year-old children in the norm sample exhibited internal
consistency reliability values ranging from 0.75 to 0.86. Test–
retest reliability for a subgroup of 4- through 7-year-old
children was 0.86. Interrater reliability was 0.98 (see Bruin-
inks & Bruininks, 2005).
Motor timing task. Each child completed a motor timing
task (Zelaznik, Spencer, & Ivry, 2008) that purportedly
reflects cerebellar function. Specifically, participants
attempted to synchronize pressing the space bar on a com-
puter keyboard to a metronome set at a rate of 2 Hz (500 ms
period). Following 10 s (19 intervals) of synchronization,
participants continued to press without the metronome for
about 20 s, attempting to maintain the rate. Participants
completed 15 to 20 trials of this task. The time series of
intertap intervals from the continuation (i.e., no metronome)
portion of the task were detrended and the coefficient of
variation was calculated. The coefficient of variation is
defined as the standard deviation of the time series divided
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
74 Journal of Attention Disorders 17(1)
by the average intertap interval, converted to a percentage.
The best six trials (i.e., smallest coefficients) were averaged
and used in the primary data analysis. Using best trials lim-
its the degree to which timing variability stems from chang-
ing participant tapping strategies within and across trials
(see Zelaznik, Spencer, & Ivry, 2002).
Shape School. The Shape School (Espy, 1997; Espy, Bull,
Martin, & Stroup, 2006) assesses set shifting and response
inhibition in young children using a storybook format that
presents tasks of increasing difficulty. Condition A assesses
cognitive-processing speed, where children are asked to
name the colors of the depicted figures. The subsequent
conditions assess executive function. Condition B requires
children to name colors of “happy-faced figures” and inhibit
naming “sad-faced figures”; Condition C requires naming
the colors of figures without hats and naming the shapes of
figures with hats; Condition D requires children to name the
colors of happy-faced figures without hats, inhibit naming
sad-faced figures without hats, name the shapes of happy-
faced figures wearing hats, and inhibit naming sad-faced
figures with hats. In the present study, the efficiency scores
(number correct divided by time to completion) for Condi-
tions A through D were analyzed. Espy et al.’s (2006) vali-
dation sample for the Shape School was 219 typically
developing preschoolers, 54% girls, 85% White, and
described as possessing varied vocabulary skills. Executive
conditions of the Shape School (i.e., all conditions except A)
had internal consistency reliability values exceeding 0.70
and showed significant correlations with other measures
purported to assess executive function (see Espy et al., 2006).
Moreover, recent work shows inattention/hyperactivity to
correspond with lower inhibitory efficiency on the Shape
School (Pritchard & Woodward, 2011).
Mazes. The Mazes subtest from either the Wechsler Pre-
school and Primary Scale of Intelligence–Revised (WPPSI-
R; Wechsler, 1989) or the Wechsler Intelligence Scale for
Children–III (WISC-III; Wechsler, 1991) was administered
depending on the child’s age. Mazes assesses “the child’s
planning ability and perceptual-organizational ability—that
is, the ability to plan and follow a visual pattern” (Sattler,
2001, p. 294). In the present study, age-normed scaled scores
(M = 10, SD = 3) were derived and analyzed with higher
scores indexing more advanced planning ability. The stan-
dardization sample for the WPPSI-R was 1,700 children
ranging in age from 3 years to 7 years 3 months; the stan-
dardization sample for the WISC-III was 2,200 children
ranging in age from 6 to 16 years (Sattler, 2001). The mean
internal consistency reliabilities for the WPPSI-R and WISC-
III Mazes, respectively, are 0.77 and 0.70 (Sattler, 2001).
Finger Windows. The Finger Windows subtest from the
Wide Range Assessment of Memory and Learning–second
edition (WRAML-2; Sheslow & Adams, 2003) was
employed as a measure of spatial working memory. Specifi-
cally, children are shown an 8 × 11 in. card that has nine
holes cut in various locations. The examiner puts a pencil
through the holes in various prespecified sequences of
increasing difficulty and the child is required to correctly
replicate each one using her or his index finger. Scores are
derived based on the number of sequences correctly repli-
cated and are converted to scaled scores (M = 10, SD = 3)
based on age norms. The standardization sample for the
WRAML-2 was 1,200 individuals, ranging in age from 5 to
90 years (Sheslow & Adams, 2003). Higher scores indicate
better spatial working memory. As reported in the test man-
ual, the coefficient alpha reliabilities of the Finger Win-
dows subtest ranged from 0.76 to 0.83 across the 5-to 8-year
-11-month age range (Sheslow & Adams, 2003).
Sentence Memory. The Sentence Memory subtest, also
from the WRAML-2 (Sheslow & Adams, 2003), was
employed as a measure of verbal working memory. Specifi-
cally, children are read a series of sentences of increasing
difficulty and asked to recall each one exactly as stated.
Points are deducted for each error of omission, commission,
or change of a word (e.g., tense, contracting two words).
Scores are derived based on the degree of correct recall and
are converted to scaled scores (M = 10, SD = 3) based on
age norms. Higher scores indicate better verbal working
memory. As reported in the test manual, the coefficient
alpha reliabilities of the Sentence Memory subtest ranged
from 0.69 to 0.78 across the 5-to 8-year-11-month age
span (Sheslow & Adams, 2003).
Numbers Reversed. The Numbers Reversed subtest from
the Woodcock–Johnson III Tests of Cognitive Abilities
(WJ-III; Woodcock, McGrew, & Mathur, 2001) was admin-
istered, which requires children to listen to sequences of
number digits read by the examiner and then to reproduce
them in reverse order. This task measures working memory
skills that require mental manipulation and also has been
shown to assess planning (e.g., Schofield & Ashman, 1986).
Scores are derived based on the degree of correct perfor-
mance and are converted to standard scores (M = 100, SD =
15) based on age norms. The standardization sample for the
WJ-III included 8,818 individuals selected to be representa-
tive of persons aged 2 to 90+ years from 100 different com-
munities spanning the Northeast, Midwest, South, and West
portions of the United States (McGrew & Woodcock,
2001). Higher scores indicate better verbal working mem-
ory. According to Appendix A of the technical manual reli-
ability coefficients for the Numbers Reversed subtest
ranged from .84 to .92 across the 5- to 8-year-old age range
in the standardization sample.
Weekly Measures
Simon Says. The proportion of inhibition failures during
the popular children’s game Simon Says was used as a
weekly ecological measure of response inhibition. This task
has been used previously in research (Strommen, 1973) to
assess inhibition errors in young children. In a given
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
Smith et al. 75
administration of the task, 30 commands were provided
with 10 instances absent the preliminary statement “Simon
Says.” These 10 instances were coded for inhibition failure,
and were differently sequenced from week to week to pre-
vent improvements stemming from memory of a command
pattern. Timing of commands was constant across an
administration of the task. Interrater reliability (agreements
divided by the sum of agreements and disagreements) was
assessed on detection of inhibition errors beginning in
Week 2 and averaged 0.94 over the remainder of the
program.
Red Light/Green Light. Similarly, the proportion of inhibi-
tion errors during a weekly Red Light/Green Light game
was used as an ecological measure of response inhibition.
Specifically, children were permitted to take one step fol-
lowing a “red light” call, and any additional steps or loss of
balance involving touching the ground (with any body part
except one’s feet) were coded as inhibition errors. In a
given administration of the task, 27 commands were pro-
vided with 15 instances being a “red light” and coded by
observers. The command sequence was changed from week
to week and the length of “green light” portions varied,
ranging from 1 to 4 s, within a session to prevent improve-
ments stemming from memory of the command pattern.
Interrater reliability was assessed on detection of inhibition
errors beginning in Week 2 and averaged 0.95 over the
remainder of the program.
Pittsburgh Modified Conners Teacher Rating Scale. Teacher
ratings of behavior on the Pittsburgh Modified Conners
Rating Scale (Pelham, 2002), which includes the widely
used 10-item Abbreviated Conners (Goyette, Conners, &
Ulrich, 1978) and Iowa Conners scales (Loney & Milich,
1982), as well as a Peer Interaction Scale developed by Pelham
(2002), were collected on at least a weekly basis. For chil-
dren with more frequent teacher ratings, items were aver-
aged to produce a weekly item score. Teachers rated on a
4-point scale (ranging from not at all to very much) the
extent to which each problem behavior was present. Scale
scores were derived separately for the Abbreviated Con-
ners, the inattention/overactivity and oppositional/defiant
subscales of the Iowa Conners, as well as the Peer Interac-
tions Scale, by averaging the computed item scores com-
prising each scale. Higher scores on these scales indicate
more problematic behavior. Pelham and colleagues (Pelham,
2002; Pelham, Milich, Murphy & Murphy, 1989) reported
means and standard deviations (n > 600) for boys and girls
in Kindergarten through fifth grade. As reported by Pelham
et al. (1989), coefficient alphas for the inattention/overac-
tivity and oppositional/defiant subscales, respectively, were
0.89 and 0.92.
Daily Observations of Behavior.
Trained behavioral observers recorded frequencies of the
following negative behaviors nicely, interrupting, intentional
aggression, unintentional aggression, and not following adult
directions. Observational methods were derived specifically
for this study to fit the ages of participants and the research
goals but were modeled after established procedures (Pelham
& Hoza, 1996). Observers received approximately 20 hr of
training and practice with the observational procedures prior
to commencing the program. Because of limited resources
and the staffing demands required to administer this pilot
program, only sporadic checks of the reliability of the obser-
vation system could be conducted. There was inconsistency
of interrater reliability values (complete disagreement
through complete agreement) across these spot checks. This
in part is likely because the checks covered short periods of
time and were infrequent, meaning a low number of overall
behaviors would be observed (i.e., any rater disagreement
would substantially impact reliability). Accordingly, results
pertaining to behavioral observations should be interpreted
with caution.
Post Program Only Improvement Ratings.
Ratings of perceived degree of improvement were com-
pleted by teachers, parents, and program staff at post pro-
gram following procedures used extensively in prior work
by Pelham and colleagues (Hoza & Pelham, 1995; Pelham
et al., 2000; Pelham & Hoza, 1996). Global ratings of over-
all improvement as well as ratings in specific areas (see
Table 2) were assessed on a 7-point scale (1 = very much
worse; 7 = very much improved). Consistent with prior
work, children for whom raters did not perceive a problem
in a given area were rated as “no problem” and not included
in analyses for that domain. In addition, both teachers and
program staff completed end-of-program ratings indexing
the degree to which each child’s behavior with (a) peers and
(b) adults was “like that of a normal child” (0 = very much
like a normal child; 6 = not at all like a normal child).
Finally, teachers and staff rated the degree to which they
found interacting with the child pleasant by the end of the
program (0 = very pleasant; 6 = very unpleasant). Parent
and teacher ratings were each provided by a single infor-
mant. As there were multiple program staff providing rat-
ings of each child, a single score for each child was derived
by averaging across raters.
Data Analysis
Dependent t tests were employed to test change over the
program on pre -post program measures, weekly measures,
and daily observations of behavior. For weekly measures,
averaged data over the first half of the program were com-
pared with averaged data over the second half of the pro-
gram. Similarly, the averaged frequency of observed
behaviors per program station over the first half of the
program was compared with the averaged frequency over
the second half of the program. In light of the relatively
limited power to detect statistically significant change over
time, effect size values were calculated and interpreted.
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
76 Journal of Attention Disorders 17(1)
Table 1. Descriptive Statistics, Dependent t-Values, and Effect Size Values for Pre–Post and First Half–Second Half Comparisons
Preprogram
or first half
Postprogram
or second half Effect
sizeb
Measure M (SD)M (SD)nat p
Pre-post program measures
BOT-2 short formc40.77 (3.35) 44.00 (5.97) 13 -3.27 <.01 0.96
Continuation timing 15.68 (9.55) 14.21 (6.47) 12 0.72 .49 0.15
Shape School, Condition A 1.25 (0.29) 1.22 (0.43) 10 0.31 .76 -0.10
Shape School, Condition B 0.75 (0.23) 0.83 (0.25) 12 -3.06 <.05 0.35
Shape School, Condition C 0.48 (0.19) 0.54 (0.18) 11 -1.78 .11 0.32
Shape School, Condition D 0.37 (0.11) 0.41 (0.16) 12 -1.42 .18 0.36
Mazesc8.43 (2.56) 9.00 (2.08) 14 -0.87 .40 0.22
Finger Windowsc8.07 (2.84) 8.29 (3.02) 14 -0.26 .80 0.08
Sentence Memoryc10.38 (3.28) 10.85 (3.08) 13 -0.79 .45 0.14
Numbers Reversedc99.50 (10.59) 104.08 (15.22) 12 -1.23 .25 0.43
Weekly measures
Simon Says 0.29 (0.22) 0.33 (0.29) 13 -0.51 .62 -0.18
Red Light/Green Light 0.33 (0.15) 0.24 (0.14) 14 2.42 <.05 0.60
PMCTRS—Abbreviated Conners 0.94 (0.53) 0.62 (0.42) 14 2.18 <.05 0.60
PMCTRS—Iowa I/O 1.20 (0.53) 0.83 (0.51) 14 2.23 <.05 0.70
PMCTRS—Iowa O/D 0.67 (0.54) 0.45 (0.44) 14 2.66 <.05 0.41
PMCTRS—Peer interactions 0.46 (0.43) 0.29 (0.31) 14 2.03 .06 0.40
Daily observational measures
Not speaking nicely 0.15 (0.12) 0.17 (0.18) 14 -0.70 .50 -0.18
Interrupting 0.23 (0.14) 0.12 (0.09) 14 4.10 <.01 0.78
Intentional aggression 0.27 (0.21) 0.30 (0.34) 14 -0.44 .67 -0.12
Unintentional aggression 0.26 (0.13) 0.21 (0.12) 14 1.96 .07 0.40
Not following adult directions 1.31 (0.86) 1.32 (0.75) 14 -0.27 .79 -0.02
Notes: PMCTRS = Pittsburgh Modified Conners Teacher Rating Scale; I/O = inattention/overactivity; O/D = oppositional/defiant.
aReduced n for some analyses because of missing data, administrator/recording error, and/or participant failure to understand directions.
bEffect size calculated by dividing pre-post difference by preprogram standard deviation (positive value assigned to finding in expected/adaptive
direction).
cScaled/standard scores.
Effect size was calculated by dividing the pre -post
program difference by the standard deviation at prepro-
gram. A positive sign was assigned to values corresponding
to change in an adaptive direction and a negative sign was
assigned to values corresponding to change in a maladap-
tive direction. Values of 0.2, 0.5, and 0.8 were interpreted
as small, medium, and large effect sizes, respectively
(Cohen, 1988).
Results
Mean and standard deviation values for pre -post program
measures, weekly measures, and daily observational mea-
sures are found in Table 1. Statistically significant pre-to-
post (or first half-to-second half) program changes were
observed for BOT-2, Shape School Condition B, Red
Light/Green Light, Abbreviated Conners, Iowa Conners
(both inattention/overactivity and oppositional/defiant sub-
scales), and Interrupting scores (see Table 1). All signifi-
cant changes were in adaptive directions. The associated
effect sizes were of small or medium magnitude, with the
exception of the BOT-2, which had a large effect size. The
BOT-2 effect size would be expected in light of the nature
of the program and provides support for the efficacy of the
physical activity intervention. Six additional measures
yielded positive effect sizes greater than 0.20, indicating
improvement that did not reach statistical significance.
Overall, the relatively larger positive effect size values
tended to correspond with measures ostensibly assessing
response inhibition/impulsiveness. There was no evidence
of an adverse effect of the program, with all negative effect
sizes being less than 0.20 in magnitude.
Table 2 shows the percentage of parents, teachers, and
staff rating participants as “somewhat,” “much,” or “very
much” improved by the program. Depending on rater and
the focal domain, from 29% to 71% of participants were
rated as exhibiting some degree of improvement. The great-
est proportions of improvement were reported for the over-
all rating. Never did more than two participants receive
parent ratings that indicated worsening on a given domain.
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
Smith et al. 77
Table 2. Percentage of Participants Perceived as Improved by
Adult Raters
Rater
Measure Parent Teacher Staff
Name calling/teasing/aggression 33 33 58
Defiance/noncompliance 42 50 69
Self-esteem 67 54 71
Responsibility 58 31 29
Social skills 67 Not rated 57
Following rules 46 54 43
Overall 69 64 71
Note: Rater indicated “somewhat improved,” “much improved,” or “very
much improved.”
For teacher ratings, on no occasion did more than one par-
ticipant receive such a rating. Normalization ratings for
behavior with peers (teacher rating: M = 2.00, SD = 1.52;
staff rating: M = 1.77, SD = 0.90) and with adults (teacher
rating: M = 2.07, SD = 1.54; staff rating: M = 1.77, SD =
0.88) corresponded to participants behaving more “like a
normal child” than not. Finally, teachers (M = 2.07, SD =
1.64) and staff (M = 1.56, SD = 0.96) rated participants
overall as relatively more pleasant than not to interact with
by the program end.
Discussion
The data obtained in the present study suggest that there is
potential value in exploring regular physical activity as a
management tool for ADHD. Specifically, our preliminary
work suggests that sustained involvement in structured
physical activity may offer benefits to motor, cognitive,
social, and behavioral functioning in young people exhibit-
ing ADHD symptoms. Almost half of the measures showed
significant or marginally significant change over the pro-
gram, with effect sizes of mostly small to medium magni-
tude. Measures of response inhibition showed the most
consistently favorable findings, suggesting that physical
activity may be especially helpful in addressing this core
manifestation of ADHD. Moreover, key informants per-
ceived some degree of improvement in overall functioning
for about two thirds of the participants. No outcomes of
meaningful effect size were in a maladaptive direction, sug-
gesting that physical activity is unlikely to be harmful when
employed as a management strategy for ADHD. In light of
the overall pattern of findings, we believe that investing in
controlled, larger-scale examinations of physical activity
effects on the management of ADHD are warranted.
The assessment of overall motor proficiency (i.e., BOT-
2; Bruininks & Bruininks, 2005) showed a large change
from pre- to postprogram. This suggests that the physical
activity intervention was designed in a way that afforded
development of fitness parameters and motor control. The
emphasis on sustained MVPA within a framework of games
and activities that employ a variety of movement skills
would be expected to yield such an outcome, and is recom-
mended for future work with young children. Designing
the intervention in this way offers the stimulus necessary to
promote fitness and motor skill development while offer-
ing the variety and challenge necessary to sustain partici-
pant interest. Also, because ADHD is a disorder
appearing to involve a variety of brain regions (see Nigg,
2006), such a dynamic physical activity intervention seems
appropriate.
Of note is the absence of intervention effect on the con-
tinuation timing task. Because this task is simplified and
highly timing dependent, it is strongly linked to cerebellar
function (Ivry, Keele, & Diener, 1988). The findings sug-
gest that if any benefits of a physical activity intervention to
motor timing and cerebellar function exist, they would be
less marked than those for broader motor functioning and/
or would require an extended intervention time frame to
detect. Pursuit of long-term intervention investigations will
offer opportunity to understand this finding better. In light
of the imprecise etiology of ADHD, it is reasonable to
expect that physical activity benefits on particular symp-
toms or brain systems would be neither uniform nor accrued
at equal rates.
A broad range of tasks were employed to measure cogni-
tive function of study participants, with measures assessing
response inhibition, set shifting, planning, and working
memory. Such executive functions were selected for exami-
nation because deficits in these functions represent core
impairments associated with ADHD (Barkley, 1997) and
emerging work indicates that fitness levels and physical
activity may be linked to their preservation and enhance-
ment (Etnier & Chang, 2009; Hillman, Buck, Themanson,
Pontifex, & Castelli, 2009). The strength of findings was
mixed across tasks. For example, working memory tasks
did not show meaningful effect size values (i.e., ≥ |0.20|)
except when involving additional cognitive load as in the
Numbers Reversed task (effect size = 0.43). The latter task
involves planning, which in children has been shown to
benefit from exercise training (Davis et al., 2007). However,
the Mazes task, which purportedly measures planning,
yielded a small effect size in our study. The more consistent
effects were found for the tasks requiring response inhibi-
tion. The effect size values for these tasks ranged from 0.35
to 0.60, with the exception of the Simon Says task. Though
we were able to reliably measure behavioral responses on
this particular task, children could correct themselves before
their behaviors were coded as inhibition failures. That is,
children could return to their previous body position before
fully adopting the body position to inhibit. Many children
appeared to use the feedback available from viewing coact-
ing peers, which essentially served as a second piece of
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
78 Journal of Attention Disorders 17(1)
information (beyond the leader’s instruction) about the cor-
rect response. The children did not have sufficient time to
make such corrections in the Red Light/Green Light task,
which was fast paced and had maximally concise com-
mands. Accordingly, we believe the latter task was a par-
ticularly sensitive ecological assessment of response
inhibition. The overall findings lead us to conclude that
physical activity may offer cognitive benefits to young chil-
dren with ADHD symptoms, particularly with regard to
executive tasks requiring inhibitory control.
Social and behavioral functioning was assessed by
weekly teacher ratings and daily observations of social
behaviors. The weekly teacher ratings showed meaningful
effect size values (0.40 to 0.70), suggesting improved
school-day social and behavioral functioning across the
time frame of the before-school physical activity program.
This finding is notable, suggesting that possible physical
activity effects on social and behavioral symptoms can
extend beyond the physical activity period and context.
Such carryover is essential if physical activity is to be a
viable option for ADHD intervention. Within the program
setting itself, the observed behaviors of interrupting (effect
size = 0.78) and unintentional aggression (effect size = 0.40)
showed meaningful improvement, whereas the behaviors
of not speaking nicely, intentional aggression, and not
following adult directions failed to meaningfully change.
Interrupting and unintentional aggressive acts are under-
pinned by impulsiveness, and therefore these behavioral
findings align closely with the response inhibition findings.
This noted, the reader is reminded that the reliability of our
behavioral coding system is uncertain and therefore the daily
behavior findings must be cautiously interpreted.
In line with the behavioral observations, fewer children
were rated by adult informants as showing some degree of
improvement over the program in name calling/teasing/
aggression and following rules than in some other areas,
although it is important to remember that only those children
who had problems in these areas could be rated as improved.
A comparable outcome was observed for responsibility.
More encouraging findings were obtained for adult ratings
of child self-esteem, social skills, and overall functioning.
Defiance/noncompliance improvement rating values were
between the values for these sets. Overall, we interpret the
ratings to offer some support for the validity of our behav-
ioral observation system and to suggest a generalized poten-
tial benefit of the before-school program to our participants.
The finding for overall functioning is comparable with
improvement percentages for children upon initial stimulant
use (Spencer et al., 1996), suggesting that physical activity
may hold promise as an intervention strategy for ADHD.
This work is clearly preliminary and must be considered in
light of its limitations, including lack of a control condition,
lack of a comparison group of typically developing peers, a
relatively small sample, and the administration of weekly tasks
(i.e., Simon Says, Red Light/Green Light) that in themselves
could improve inhibitory control independent of the physical
activity intervention. Important delimitations include the
exclusive enrollment of young children and those exhibiting
hyperactivity/impulsivity in the study. The present work can-
not speak of possible physical activity benefits to older chil-
dren or those with the inattentive-only form of ADHD.
However, we believe the results stemming from this pilot
before-school physical activity program are noteworthy
because (a) all meaningful changes were in adaptive direc-
tions, (b) the lack of meaningful maladaptive outcomes sug-
gests such an intervention program has low probability of
doing harm to participants, (c) observable changes were
detected despite a small sample size, and (d) none of the med-
ication naïve participants initiated medication use over the
span of the program, though families were free to pursue this
option. Moreover, these encouraging program outcomes are
observed within an intervention context yielding high partici-
pant adherence. Program attendance was strong, suggesting
that reasonable adherence to a physical activity intervention
protocol can be expected in a before-school setting over
about a 2-month time frame. Future work to assess adherence
rates over longer time spans is warranted, as chronic physical
activity participation will be necessary to manage persistent
ADHD symptoms.
Beyond an extended and controlled field investigation of
physical activity intervention effects on ADHD symptoms
that addresses the limitations noted above, future work
would be strengthened by adopting a translational approach
where basic research on mechanisms of ADHD and physical
activity effects on behavior, as well as brain function and
development, informs clinical research. Recent animal work
by Hopkins, Sharma, Evans, and Bucci (2009), for example,
showed exercise to yield adaptive effects on attention and
social behavior in female mice possessing behavioral and
neurobiological characteristics of ADHD compared with
controls. The effects were not observed in male counter-
parts, suggesting that sex may be an important moderator of
physical activity effects on ADHD. This finding should be
carefully explored within human intervention trials. If physi-
cal activity is established as an effective intervention for
ADHD, it will also be important to address possible comple-
mentary effects of physical activity and existing treatment
strategies, dose–response relationships, costs of physical
activity intervention, long-term adherence challenges, and
developmental implications of early physical activity inter-
vention among other issues. We believe that the present pilot
findings justify the expenditure of time, effort, and resources
necessary to pursue these interesting and clinically valuable
research avenues.
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
Smith et al. 79
Acknowledgments
The authors thank David Bucci and John Green for thoughtful
feedback on an earlier draft of the manuscript, J. D. DeF reese for assis-
tance with assessment scoring, and Howard Zelaznik for guidance
on the motor timing methods and analysis. Finally, the authors
thank the participating children, parents, teachers, and school
administrators that contributed to the success of this project.
Authors’ Notes
Aaron J. Vaughn is now with the Division of Behavioral Medicine,
Cincinnati Children’s Hospital, OH. Meghan Tomb is now with
Columbia University Medical Center/New York Presbyterian
Hospital. She completed her PhD and is now a post doc there.
Hoza and Smith are equal contributors to this research.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: The
authors thank the McNeil Prevention and Community Psychology
Fund at the University of Vermont for supporting this research.
References
Achenbach, T. M. (1991). Manual for the child behavior checklist/4-18
and 1991 profile. Burlington: University of Vermont.
American Academy of Pediatrics. (2000). Clinical practice guide-
line: Diagnosis and evaluation of the child with attention-
deficit/hyperactivity disorder. Pediatrics, 105, 1158-1170.
doi:10.1542/peds.105.5.1158
American Psychiatric Association. (2000). Diagnostic and statis-
tical manual of mental disorders (4th ed. text rev.). Washing-
ton, DC: Author.
Barkley, R. A. (1997). Behavioral inhibition, sustained attention,
and executive functions: Constructing a unifying theory of
ADHD. Psychological Bulletin, 121, 65-94. doi:10.1037/0033-
2909.121.1.65
Best, J. R. (2010). Effects of physical activity on children’s
executive function: Contributions of experimental research
on aerobic exercise. Developmental Review, 30, 331-351.
doi:10.1016/j.dr.2010.08.001
Biederman, J., Spencer, T. J., Wilens, T. E., Prince, J. B., &
Faraone, S. V. (2006). Treatment of ADHD with stimu-
lant medications: Response to Nissen perspective in The
New England Journal of Medicine. Journal of the American
Academy of Child & Adolescent Psychiatry, 45, 1147-1150.
doi:10.1097/01.chi.0000227883.88521.e6
Bruininks, R. H., & Bruininks, B. D. (2005). BOT2 Bruininks-
Oseretsky test of motor proficiency (2nd ed.). Minneapolis,
MN: Pearson.
Calfas, K. J., & Taylor, W. C. (1994). Effects of physical activity
on psychological variables in adolescents. Pediatric Exercise
Science, 6, 406-423.
Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985).
Physical activity, exercise, and physical fitness: Definitions
and distinctions for health-related research. Public Health
Reports, 100, 126-130.
Centers for Disease Control and Prevention. (2010). The associa-
tion between school-based physical activity, including physi-
cal education, and academic performance. Atlanta, GA: U.S.
Department of Health and Human Services.
Cohen, J. (1988). Statistical power analysis for the behavioral sci-
ences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Colcombe, S. J., Erickson, K. I., Scalf, P. E., Kim, J. S., Prakash, R.,
McAuley, E., . . . Kramer, A. F. (2006). Aerobic exercise train-
ing increases brain volume in aging humans. Journal of Ger-
ontology: Medical Sciences, 61A, 1166-1170.
Colcombe, S. J., Kramer, A. F., Erickson, K. I., Scalf, P.,
McAuley, E., Cohen, N. J., . . . Elavsky, S. (2004). Cardiovas-
cular fitness, cortical plasticity, and aging. Proceedings of the
National Academy of Sciences, 101, 3316-3321. doi:10.1073/
pnas.0400266101
Conners, C. K., Epstein, J. N., March, J. S., Angold, A., Wells, K. C.,
Klaric, J., . . . Wigal, T. (2001). Multimodal treatment of
ADHD in the MTA: An alternative outcome analysis. Journal
of the American Academy of Child & Adolescent Psychiatry,
40, 159-167. doi:10.1097/00004583-200102000-00010
Cotman, C. W., Berchtold, N. C., & Christie, L.-A. (2007). Exer-
cise builds brain health: Key roles of growth factor cascades
and inflammation. Trends in Neurosciences, 30, 464-472.
doi:10.1016/j.tins.2007.06.011
Davis, C. L., Tomporowski, P. D., Boyle, C. A., Waller, J. L.,
Miller, P. H., Naglieri, J. A., & Gregoski, M. (2007). Effects
of aerobic exercise on overweight children’s cognitive func-
tioning: A randomized controlled trial. Research Quarterly for
Exercise and Sport, 78, 510-519.
Diamond, A. (2000). Close interrelation of motor development
and cognitive development and of the cerebellum and prefron-
tal cortex. Child Development, 71, 44-56. doi:10.1111/1467-
8624.00117
Espy, K. A. (1997). The Shape School: Assessing executive func-
tion in preschool children. Developmental Neuropsychology,
13, 495-499. doi:10.1080/87565649709540690
Espy, K. A., Bull, R., Martin, J., & Stroup, W. (2006). Measur-
ing the development of executive control with the Shape
School. Psychological Assessment, 18, 373-381. doi:10.1037/
1040-3590.18.4.373
Etnier, J. L., & Chang, Y.-K. (2009). The effect of physical activ-
ity on executive function: A brief commentary on definitions,
measurement issues, and the current state of the literature.
Journal of Sport & Exercise Psychology, 31, 469-483.
Ferris, L. T., Williams, J. S., & Shen, C.-L. (2007). The effect
of acute exercise on serum brain-derived neurotrophic factor
levels and cognitive function. Medicine & Science in Sports &
Exercise, 39, 728-734. doi:10.1249/mss.0b013e31802f04c7.
Gapin, J., & Etnier, J. L. (2010). The relationship between physi-
cal activity and executive function performance in children
with attention-deficit hyperactivity disorder. Journal of Sport
& Exercise Psychology, 32, 753-763.
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
80 Journal of Attention Disorders 17(1)
Goyette, C. H., Conners, C. K., & Ulrich, R. F. (1978). Norma-
tive data on the revised Conners parent and teacher rating
scales. Journal of Abnormal Child Psychology, 6, 221-236.
doi:10.1007/BF00919127
Hall, C. D., Smith, A. L., & Keele, S. W. (2001). The impact
of aerobic activity on cognitive function in older adults: A
new synthesis based on the concept of executive control.
European Journal of Cognitive Psychology, 13, 279-300.
doi:10.1080/09541440126012
Halperin, J. M., & Healey, D. M. (2011). The influences of
environmental enrichment, cognitive enhancement, and
physical exercise on brain development: Can we alter the
developmental trajectory of ADHD? Neuroscience and
Biobehavioral Reviews, 35, 621-634. doi:10.1016/j.neubiorev
.2010.07.006.
Harvey, W. J., & Reid, G. (2003). Attention-deficit/hyperactivity dis-
order: A review of research on movement skill performance and
physical fitness. Adapted Physical Activity Quarterly, 20, 1-25.
Hillman, C. H., Buck, S. M., Themanson, J. R., Pontifex, M. B.,
& Castelli, D. M. (2009). Aerobic fitness and cognitive devel-
opment: Event-related brain potential and task performance
indices of executive control in preadolescent children. Devel-
opmental Psychology, 45, 114-129. doi:10.1037/a0014437
Hopkins, M. E., Sharma, M., Evans, G. C., & Bucci, D. J. (2009).
Voluntary physical exercise alters attentional orienting and
social behavior in a rat model of attention-deficit/hyperactivity
disorder. Behavioral Neuroscience, 123, 599-606. doi:10.1037/
a0015632
Hoza, B., Owens, J. S., & Pelham, W. E. (1999). Attention-deficit/
hyperactivity disorder. In R. T. Ammerman, M. Hersen, &
C. G. Last (Eds.), Handbook of prescriptive treatments for
children and adolescents (2nd ed., pp. 63-83). Boston, MA:
Allyn & Bacon.
Hoza, B., & Pelham, W. E. (1995). Social-cognitive predictors of
treatment response in children with ADHD. Journal of Social
and Clinical Psychology, 14, 23-35.
Ivry, R. B., Keele, S. W., & Diener, H. C. (1988). Dissociation of the
lateral and medial cerebellum in movement timing and move-
ment execution. Experimental Brain Research, 73, 167-180.
doi:10.1007/BF00279670
Jensen, P. S., Arnold, L. E., Swanson, J. M., Vitiello, B.,
Abikoff, H. B., Greenhill, L. L., . . . Hur, K. (2007). 3-year
follow-up of the NIMH MTA study. Journal of the American
Academy of Child & Adolescent Psychiatry, 46, 989-1002.
doi:10.1097/CHI.0b013e3180686d48
Kiluk, B. D., Weden, S., & Culotta, V. P. (2009). Sport participa-
tion and anxiety in children with ADHD. Journal of Attention
Disorders, 12, 499-506. doi:10.1177/1087054708320400
Kim, Y.-P., Kim, H., Shin, M.-S., Chang, H.-K., Jang, M.-H., Shin,
M.-C., . . . Kim, C-J (2004). Age-dependence of the effect of
treadmill exercise on cell proliferation in the dentate gyrus of
rats. Neuroscience Letters, 355, 152-154. doi:10.1016/j.neu-
let.2003.11.005
Kramer, A. F., Hahn, S., Cohen, N. J., Banich, M. T.,
McAuley, E., Harrison, C. R., & Colcombe, A. (1999). Age-
ing, fitness and neurocognitive function. Nature, 400, 418-419.
doi:10.1038/22682
Loney, J., & Milich, R. (1982). Hyperactivity, inattention, and
aggression in clinical practice. In M. Wolrich & D. K. Routh
(Eds.), Advances in developmental and behavioral pediatrics
(Vol. 3, pp 113-147). Greenwich, CT: JAI Press.
Mannuzza, S., & Klein, R. G. (2000). Long-term prognosis in
attention-deficit/hyperactivity disorder. Child and Adolescent
Psychiatric Clinics of North America, 9, 711-726.
McGrew, K. S., & Woodcock, R. W. (2001). Technical Manual.
Woodcock-Johnson III. Itasca, IL: Riverside.
MTA Cooperative Group. (1999a). A 14-month randomized clini-
cal trial of treatment strategies for attention-deficit/hyperactivity
disorder. Archives of General Psychiatry, 56, 1073-1086.
doi:10.1001/archpsyc.56.12.1073
MTA Cooperative Group. (1999b). Moderators and mediators
of treatment response for children with attention-deficit/
hyperactivity disorder. Archives of General Psychiatry, 56,
1088-1096. doi:10.1001/archpsyc.56.12.1088
MTA Cooperative Group. (2004). National institute of men-
tal health multimodal treatment study of ADHD follow-up:
24-month outcomes of treatment strategies for attention-deficit/
hyperactivity disorder. Pediatrics, 113, 754-761. doi:10.1542/
peds.113.4.754
National Institutes of Health. (2000). National institutes of
health consensus development conference statement: Diag-
nosis and treatment of attention-deficit/hyperactivity disor-
der (ADHD). Journal of the American Academy of Child &
Adolescent Psychiatry, 39, 182-193. doi:10.1097/00004583-
200002000-00018
Nigg, J. T. (2006). What causes ADHD? Understanding what goes
wrong and why. New York, NY: Guilford.
Nissen, S. E. (2006). ADHD drugs and cardiovascular risk. New
England Journal of Medicine, 354, 1445-1448. doi:10.1056/
NEJMp068049
Olson, A. K., Eadie, B. D., Ernst, C., & Christie, B. R. (2006).
Environmental enrichment and voluntary exercise massively
increase neurogenesis in the adult hippocampus via dissociable
pathways. Hippocampus, 16, 250-260. doi:10.1002/hipo.20157
Pelham, W. E. (2002). Attention deficit hyperactivity disorder:
Diagnosis, assessment, nature, etiology, and treatment. Buffalo,
NY: University at Buffalo.
Pelham, W. E., Foster, E. M., & Robb, J. A. (2007). The economic
impact of attention-deficit/hyperactivity disorder in children
and adolescents. Journal of Pediatric Psychology, 32, 711-727.
doi:10.1093/jpepsy/jsm022
Pelham, W. E., Gnagy, E. M., Greenslade, K. E., & Milich, R.
(1992). Teacher ratings of DSM-III-R symptoms for the
disruptive behavior disorders. Journal of the American
Academy of Child & Adolescent Psychiatry, 31, 210-218.
doi:10.1097/00004583-199203000-00005
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
Smith et al. 81
Pelham, W. E., Gnagy, E. M., Greiner, A. R., Hoza, B.,
Hinshaw, S. P., Swanson, J. M., . . . McBurnett, K. (2000).
Behavioral versus behavioral and pharmacological treatment
in ADHD children attending a summer treatment program. Journal
of Abnormal Child Psychology, 28, 507-525. doi:10.1023/
A:1005127030251
Pelham, W. E., & Hoza, B. (1996). Intensive treatment: A summer
treatment program for children with ADHD. In E. D. Hibbs
& P. S. Jensen (Eds.), Psychosocial treatments for child and
adolescent disorders: Empirically based strategies for clinical
practice (pp. 311-340). Washington, DC: American Psycho-
logical Association.
Pelham, W. E., Milich, R., Murphy, D. A., & Murphy, H. A.
(1989). Normative data on the IOWA Conners Teacher Rat-
ing Scale. Journal of Clinical Child Psychology, 18, 259-262.
Pereira, A. C., Huddleston, D. E., Brickman, A. M., Sosunov, A. A.,
Hen, R., McKhann, G. M., . . . Small, S. A. (2007). An in vivo
correlate of exercise-induced neurogenesis in the adult dentate
gyrus. Proceedings of the National Academy of Sciences, 104,
5638-5643. doi:10.1073/pnas.0611721104
Pritchard, V. E., & Woodward, L. J. (2011). Preschool executive
control on the Shape School task: Measurement consider-
ations and utility. Psychological Assessment, 23, 31-43. doi:
10.1037/a0021095
Richters, J. E., Arnold, L. E., Jensen, P. S., Abikoff, H.,
Conners, C. K., Greenhill, L. L., . . . Swanson, J. M. (1995).
NIMH collaborative multisite multimodal treatment study of
children with ADHD: I. Background and rationale. Journal of
the American Academy of Child & Adolescent Psychiatry, 34,
987-1000. doi:10.1097/00004583-199508000-00008
Sattler, J. M. (2001). Assessment of children: Cognitive applica-
tions (4th ed.). San Diego, CA: Jerome M. Sattler.
Schachar, R., Jadad, A. R., Gauld, M., Boyle, M., Booker, L.,
Snider, A., . . . Cunningham, C. (2002). Attention-deficit
hyperactivity disorder: Critical appraisal of extended treat-
ment studies. Canadian Journal of Psychiatry, 47, 337-348.
Schofield, N. J., & Ashman, A. F. (1986). The relationship between
digit span and cognitive processing across ability groups.
Intelligence, 10, 59-73. doi:10.1016/0160-2896(86)90027-9
Seidman, L. J., Valera, E. M., & Makris, N. (2005). Structural
brain imaging of attention-deficit/hyperactivity disorder.
Biological Psychiatry, 57, 1263-1272. doi:10.1016/j.bio-
psych.2004.11.019
Sheslow, D., & Adams, W. (2003). Wide range assessment of
memory and learning (2nd ed.). Los Angeles, CA: Western
Psychological Services.
Sibley, B. A., & Etnier, J. L. (2003). The relationship between
physical activity and cognition in children: A meta-analysis.
Pediatric Exercise Science, 15, 243-256.
Spencer, T., Biederman, J., Wilens, T., Harding, M., O’Donnell,
D., & Griffin, S. (1996). Pharmacotherapy of attention-deficit
hyperactivity disorder across the life cycle. Journal of the
American Academy of Child & Adolescent Psychiatry, 35,
409-432. doi:10.1097/00004583-199604000-00008
Stensel, D. J., Gorely, T., & Biddle, S. J. H. (2008). Youth health
outcomes. In A. L. Smith & S. J. H. Biddle (Eds.), Youth phys-
ical activity and sedentary behavior: Challenges and solutions
(pp. 31-57). Champaign, IL: Human Kinetics.
Strommen, E. A. (1973). Verbal self-regulation in a children’s
game: Impulsive errors on “Simon Says.” Child Development,
44, 849-853. doi:10.2307/1127737
Strong, W. B., Malina, R. M., Blimkie, C. J. R., Daniels, S. R.,
Dishman, R. K., Gutin, B., . . . Trudeau, F. (2005). Evidence
based physical activity for school-age youth. Journal of Pedi-
atrics, 146, 732-737. doi:10.1016/j.jpeds.2005.01.055
Swanson, J., Greenhill, L., Wigal, T., Kollins, S., Stehli, A.,
Davies, M., . . . Wigal, S. (2006). Stimulant-related reduc-
tions of growth rates in the PATS. Journal of the American
Academy of Child & Adolescent Psychiatry, 45, 1304-1313.
doi:10.1097/01.chi.0000235075.25038.5a
Swanson, J. M., Kraemer, H. C., Hinshaw, S. P., Arnold, L. E.,
Conners, C. K., Abikoff, H. B., . . . Wu, M. (2001). Clinical
relevance of the primary findings of the MTA: Success rates
based on severity of ADHD and ODD symptoms at the end
of treatment. Journal of the American Academy of Child &
Adolescent Psychiatry, 40, 168-179. doi:10.1097/00004583-
200102000-00011
Tang, S. W., Chu, E., Hui, T., Helmeste, D., & Law, C. (2008).
Influence of exercise on serum brain-derived neurotrophic
factor concentrations in healthy human subjects. Neuroscience
Letters, 431, 62-65. doi:10.1016/j.neulet.2007.11.019
Tantillo, M., Kesick, C. M., Hynd, G. W., & Dishman, R. K.
(2002). The effects of exercise on children with attention-
deficit hyperactivity disorder. Medicine & Science in
Sports & Exercise, 34, 203-212. doi:10.1097/00005768-
200202000-00004
Tomporowski, P. D., Davis, C. L., Miller, P. H., & Naglieri, J. A.
(2008). Exercise and children’s intelligence, cognition, and
academic achievement. Educational Psychology Review, 20,
111-131. doi:10.1007/s10648-007-9057-0
Wechsler., D. (1989). Wechsler Preschool and Primary Scale of
Intelligence–Revised. San Antonio, TX: Psychological Corpo-
ration.
Wechsler, D. (1991). Intelligence Scale for Children (3rd ed.). San
Antonio, TX: Psychological Corporation.
Wigal, S. B., Nemet, D., Swanson, J. M., Regino, R.,
Trampush, J., Ziegler, M. G., & Cooper, D. M. (2003). Cat-
echolamine response to exercise in children with attention def-
icit hyperactivity disorder. Pediatric Research, 53, 756-761.
doi:10.1203/01.PDR.0000061750.71168.23
Wigal, T., Greenhill, L., Chuang, S., McGough, J., Vitiello, B.,
Skrobala, A., . . . Stehli, A. (2006). Safety and tolerability of
methylphenidate in preschool children with ADHD. Journal
of the American Academy of Child & Adolescent Psychiatry,
45, 1294-1303. doi:10.1097/01.chi.0000235082.63156.27
Woodcock, R. W., McGrew, K. S., & Mather, N. (2001).
Woodcock-Johnson III tests of cognitive abilities. Itasca, IL:
Riverside.
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from
82 Journal of Attention Disorders 17(1)
Zelaznik, H. N., Spencer, R. M. C., & Ivry, R. B. (2002). Dis-
sociation of explicit and implicit timing in repetitive tapping
and drawing movements. Journal of Experimental Psychology:
Human Perception and Performance, 28, 575-588. doi:10.1037/
0096-1523.28.3.575
Zelaznik, H. N., Spencer, R. M. C., & Ivry, R. B. (2008). Behavioral
analysis of human movement timing. In S. Grondin (Ed.), Psy-
chology of time (pp. 233-260). Bingley, UK: Emerald Group.
Bios
Alan L. Smith, PhD, is a professor of health and kinesiology at Purdue
University. His research addresses social and motivational aspects
of children’s sport involvement and physical activity behavior.
Betsy Hoza, PhD, is a professor of psychology at the University
of Vermont. Her research addresses mechanisms of ADHD, as
well as treatment outcomes, from a developmental psychopathol-
ogy perspective.
Kate Linnea, BA, is a doctoral student in clinical psychology at the
University of Vermont. Her research examines peer relationships in
children with ADHD and the role of sport involvement and skill.
Julia D. McQuade, BA, is a doctoral student in clinical psychol-
ogy at the University of Vermont. Her research addresses cogni-
tive risk and protective factors of emotional and social adjustment
for children with ADHD from a developmental psychopathology
perspective.
Meghan Tomb, PhD, is a postdoctoral fellow at Columbia
University Medical Center/New York Presbyterian Hospital. Her
research interests include social and cognitive processes of chil-
dren with ADHD and related disorders.
Aaron J. Vaughn, PhD, is an assistant professor at Cincinnati
Children’s Hospital Medical Center. His research pertains to
assessment and treatment of ADHD.
Erin K. Shoulberg, MA, is a doctoral student in developmental
psychology at the University of Vermont. Her research examines
how children’s and adolescents’ relationships influence their
social and academic functioning.
Holly Hook, MS, is a physical education and health teacher in
Indiana.
at MICHIGAN STATE UNIV LIBRARIES on December 11, 2012jad.sagepub.comDownloaded from