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Examining the Impact of Integrating Physical Activity on Fluid Intelligence and Academic Performance in an Elementary School Setting: A Preliminary Investigation

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To examine the impact of integrating physical activity with elementary curricula on fluid intelligence and academic achievement. A random sample of 3rd grade teachers integrated physical activity into their core curricula approximately 30 minutes a day, 3 days a week from January 2008 to April 2008. Noninvasive fluid intelligence cognitive measures were used along with State-mandated academic achievement tests. Experimental Group children averaged close to 1200 pedometer steps per integration day, thus averaging 3600 steps per week. Children in the Experimental Group performed significantly better on the SPM Fluid Intelligence Test. Children in the Experimental Group also performed significantly better on the Social Studies State mandated academic achievement test. Experimental Group children also received higher scores on the English/Language Arts, Math and Science achievements tests, but were not statistically significant compared with Control Group children. Children classified in Fitnessgram's Healthy Fitness Zone for BMI earned lower scores on many of the SPM Fluid Intelligence components. This investigation provides evidence that movement can influence fluid intelligence and should be considered to promote cognitive development of elementary-age children. Equally compelling were the differences in SPM Fluid Intelligence Test scores for children who were distinguished by Fitnessgram's BMI cut points.
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343
Reed is with the Dept of Health and Exercise Science, Furman
University, Greenville, SC. Einstein and Hahn are with the Dept
of Psychology, Furman University, Greenville, SC. Hooker is
with the Prevention Research Center, University of South Caro-
lina, Columbia, SC. Gross and Kravitz are Furman Advantage
Fellows, Furman University, Greenville, SC.
Journal of Physical Activity and Health, 2010, 7, 343-351
© 2010 Human Kinetics, Inc.
Examining the Impact of Integrating Physical Activity
on Fluid Intelligence and Academic Performance in an
Elementary School Setting: A Preliminary Investigation
Julian A. Reed, Gilles Einstein, Erin Hahn, Steven P. Hooker,
Virginia P. Gross, and Jen Kravitz
Purpose: To examine the impact of integrating physical activity with elementary curricula on uid intelligence
and academic achievement. Methods: A random sample of 3rd grade teachers integrated physical activity
into their core curricula approximately 30 minutes a day, 3 days a week from January 2008 to April 2008.
Noninvasive uid intelligence cognitive measures were used along with State-mandated academic achieve-
ment tests. Results: Experimental Group children averaged close to 1200 pedometer steps per integration
day, thus averaging 3600 steps per week. Children in the Experimental Group performed signicantly better
on the SPM Fluid Intelligence Test. Children in the Experimental Group also performed signicantly better
on the Social Studies State mandated academic achievement test. Experimental Group children also received
higher scores on the English/Language Arts, Math and Science achievements tests, but were not statistically
signicant compared with Control Group children. Children classied in Fitnessgram’s Healthy Fitness Zone
for BMI earned lower scores on many of the SPM Fluid Intelligence components. Discussion: This investiga-
tion provides evidence that movement can inuence uid intelligence and should be considered to promote
cognitive development of elementary-age children. Equally compelling were the differences in SPM Fluid
Intelligence Test scores for children who were distinguished by Fitnessgram’s BMI cut points.
Keywords: physical activity, physical education, health promotion
Participating in regular physical activity is a neces-
sary preventive behavior for youth to reduce the risks
of developing chronic diseases while increasing the
quality and perhaps the longevity of one’s life. Recent
data presented by the Centers for Disease Control and
Prevention (CDC)1 has revealed that the prevalence of
overweight youth is increasing: for children age 2 to 5,
prevalence increased from 5.0% in 1980% to 13.9% in
2004; for those age 6 to 11, prevalence increased from
6.5% to 18.8%; and for those age 12 to 19, prevalence
increased from 5.0% to 17.4% during the same time
span, respectively. Furthermore, overweight children and
adolescents are more likely to have risk factors associated
with cardiovascular disease (such as high blood pressure,
high cholesterol, and Type 2 diabetes) than are other chil-
dren and adolescents and are more likely to become obese
adults. According to the National Institute of Diabetes
and Digestive and Kidney Diseases (NIDDK), more than
80% of individuals with Type 2 diabetes are overweight.2
In addition, the CDC estimates that 48.3 million
Americans will have diabetes by the year 2050.3 One
study revealed that approximately 80% of children who
were overweight at ages 10 to 15 were obese adults at
age 25 years old.4 Another study found that 25% of obese
adults were overweight as children.5 The latter study
also found that if overweight begins before the age of 8,
obesity in adulthood is likely to be more severe. More
than 65% of American adults are obese or overweight
according to the CDC’s recent calculations.1 Despite the
proven benets of physical activity, more than one-third
of young people in grades 9 to 12 do not regularly engage
in vigorous physical activity. Available data suggest the
prevalence of obesity is more related to a lack of physical
activity than increased food intake alone.6 Regrettably,
youth spend more of their leisure time playing video-
games, watching television and engaging in sedentary
activities which are linked to the current childhood
obesity epidemic. Childhood obesity is one of the most
dangerous health threats facing youth, considering that
approximately 25 million kids are overweight or obese.6
Numerous studies have examined the impact of
physical activity on brain plasticity resulting in the identi-
cation of a variety of therapeutic enhancements. Move-
ment has been documented to increase brain-derived
neurotrophic factor (BDNF) which enhances learning
344 Reed et al
and cognition which, ironically BDNF is regulated by
physical activity.7–9 Furthermore, regular physical activ-
ity has been found to promote structural changes in the
hippocampus region of the brain, which is an important
area for memory.7 Regular physical activity has also
been found to increase neurons, dendrites and synapses-
essential structural elements located throughout the
central and peripheral nervous systems.7–9
More than 3 decades ago, Gabbard and Barton10
found a positive correlation between physical activity
and school performance; yet elementary school children
remain sedentary throughout the school day.11 A recent
review paper by Sibley and Etnier12 on this topic found
that exercise training is signicantly linked to improved
cognition in youth. Being an overweight child has also
been reported to be associated with poor IQ test per-
formance.13,14 Judge and Jahns15 recently examined the
associations between overweight children and academic
performance from recent data collected in the Early
Childhood Longitudinal Study and these data reveal that
overweight 3rd grade children had signicantly lower
math and reading tests scores in comparison with non-
overweight children in the same grade. A program entitled
Collaborating with Classroom Teachers to Increase
Daily Physical Activity: The Gear Program discovered
that integrating physical activity into the classroom can
invigorate students, as well as providing positive effects
on student learning.16 In addition, Blakemore17 reported
that the brain is activated during physical activity by
increasing blood ow to essential areas that stimulate
learning. Strong associations between the cerebellum and
memory, spatial perception, language attention, emotion,
nonverbal cues and the decision making ability of students
have also been found.9,17
An established relationship between physical activity
and the cognitive abilities of the cerebellum has also been
identied. Research also suggests that the increased blood
ow as a result of movement enhances the cerebellum
by promoting specic cognitive functions.9,17,18 Carlson
and colleagues19 recently investigated the link between
time spent in physical education and academic achieve-
ment from data collected on children from kindergarten
through 5th grade and discovered a signicant increase in
academic achievement in math and reading among girls
enrolled in higher amounts of weekly physical educa-
tion. Researchers at the RAND Institute identied that
overweight kindergartners had signicantly lower math
and reading test scores in comparison with children who
were not overweight.20 Kolb and Whinshaw21 a decade
ago, discussed how the pattern of neural specialization
often referred to as the pruning of synapses in the nervous
system can be determined in part, by environmental stim-
ulation. Moreover, Hillman and colleagues22 examined
EEG brain activity in children who were considered to
have a high level and low level of tness while performing
a choice-reaction test. Children who were considered to
possess a high level of tness in their study performed
this task more rapidly and had larger P3 amplitudes that
are consistent with enhanced executive functioning.
Unfortunately, the levels of overweight youth con-
tinue to increase at a striking pace. Studies document
that students in the US are signicantly less active in
comparison with their Australian and Swedish peers23
and have significantly higher BMI values. Physical
activity, like many behaviors, is complex and inuenced
by a number of variables. To change an individual’s
activity patterns, the behavior must be modied. With a
signicant percentage of American children inactive the
probability of them continuing this trend into adulthood
is signicant.5,11
The purpose of the current study was to examine the
impact of integrating physical activity with elementary
curricula on uid intelligence and academic performance
in an elementary school setting. Fluid Intelligence mea-
sures the ability to reason quickly and abstractly. It is
thought to be a critically important component of intel-
ligence, and it assesses one’s ability to solve problems in
situations that are not heavily dependent on previously
learned knowledge. A secondary purpose of the current
study was to examine if uid intelligence and academic
achievement scores varied by BMI values of 3rd grade
elementary students.
Methods
Sample
A random sample of (n = 155) 3rd grade students from (n
= 6) classrooms participated in the current study. Three
classrooms comprised of (n = 80) students were randomly
assigned to the Experimental Group. Three classrooms
comprised of (n = 75) students were randomly assigned to
serve as Controls. The Experimental Group in the current
study integrated physical activity (ie, fundamental skills:
running, hopping, walking) into their core curricula (ie,
Language Arts, Math, and Social Studies) approximately
30 minutes a day, 3 days a week beginning January 22,
2008, and ending April 25, 2008. All experimental group
children performed each of the physical activities during
the integrative lessons. Random audits by direct observa-
tion were used to monitor delity of intervention delivery.
All physical activities in the current study were performed
in the classroom with no equipment. The movement forms
(ie, fundamental skills) described previously is consistent
with the current types of physical activities performed
regularly in elementary school settings.24
Procedures
Personal information collected from the current study was
unidentiable per the use of systematic coding and was
only available to the research team. This process served
to limit contamination by both Experimental and Control
classroom teachers. Permission was sought and received
from the school’s principal before gathering data. Human
Subject’s Review Committee protocol requirements were
met at the university and school district level before data
collection. Student identication numbers were used to
maintain subject condentiality.
Fluid Intelligence and Academic Performance 345
Physical Activity Measures
Physical activity in the Experimental Group was mea-
sured with a NEW LIFESTYLES DIGI-WALKER
pedometer25 model number SW-200. The pedometer
was worn on the hip and measured vertical accelera-
tion, recording a step each time the hip moved up and
down. Pedometers have a suspended arm mechanism
inside the counter (similar to a clock pendulum), which
detects steps and other movements. The DIGI-WALKER
pedometer, manufactured by Yamax Inc., Tokyo, Japan,
was chosen for its accuracy and reliability in calculating
daily steps taken. The Yamax brand step counter, DW-
model (predecessor to the SW-model), measured number
of steps and distance covered within 1% accuracy rate
for adults on a sidewalk course.26 Researchers27 found a
high correlation (r = .95) between pedometer readings and
behavioral observation of physical activity with children
age 9 to 11. Kilanowski et al27 conrmed pedometers
were a valid method of measuring large samples and
a good source of feedback for intervention studies. In
addition, the unobtrusive size and economical cost makes
the pedometer a useful objective measure of children’s
physical activity. Children assigned to the Experimental
Group were properly trained to wear the pedometer.
The pedometer was reset to zero before beginning the
integration activity and the steps were recorded by each
Experimental Group teacher immediately following the
lesson and compiled in a notebook for the researchers.
The pedometer remained closed during the movement
activity. Although the pedometer selected was a valid
and reliable method to measure physical activity in the
current study, activity intensity, duration and frequency
were not allowable outcome measures with this particular
device and, therefore, were not collected.
Previous Day Physical Activity Recall
(PDPAR)
The PDPAR was used to assess the perceptions of physi-
cal activity of the children in both the Experimental and
Control Groups in the current study. The PDPAR was
administered during the rst and last week of the cur-
rent study resulting in 2 administrations. The rationale
for administering the PDPAR was to identify physical
activity differences of children in both groups that could
have inuenced the results. The purpose of the PDPAR is
to evaluate physical activity from the previous day after
school. MET values are assigned to all of the activities
and summed to compute 1 score for each child. The
Compendium of Physical Activities: Classication of
Energy Costs of Human Physical Activities was used to
validate MET values.28
Fluid Intelligence
Fluid Intelligence measures the ability to reason quickly
and abstractly. It is generally regarded as an important
component of intelligence, and it assesses one’s ability to
solve problems in situations that are not heavily depen-
dent on previously learned knowledge. The Standard
Progressive Matrices (SPM) Test designed by Raven,
Raven and Court29 has been used for decades in more
than 2500 published research studies. The SPM test used
in the current study was designed to measure eductive
components of general intelligence and cognitive ability.
Eductive ability, according to Raven et al29 is the ability
to forge new insights, the ability to discern meaning in
confusion, the ability to perceive and the ability to iden-
tify relationships.
The SPM Fluid Intelligence Test was designed for
homes, schools and workplaces as well as in laboratory
settings. The SPM Fluid Intelligence Test is comprised of
a given sets or series of diagrammatic puzzles exhibiting
a serial change in 2 dimensions simultaneously. Each
puzzle has a part missing, which the person taking the
test has to nding among the options provided.29 The
Standard Test consists of 60 problems divided into 5
sets of 12 questions each (A, B, C, D, and E). A Total
score was derived in the current study by summing all 5
sets to identify and overall score as well as specic set
scores. Over 40 studies examining the reliability of the
SPM Fluid Intelligence Test have been reported in the
literature. The general ndings include sound reliability
in regards to internal consistency and retest reliability.29
The SPM Fluid Intelligence Test was administered
at the onset of the current study on January 22nd to the
Experimental and Control Groups and was readminis-
tered during the nal week in April. Regrettably, consider-
able amounts of children in both groups were absent from
school as a result of illness, poor weather, etc during the
initial administration of the SPM Fluid Intelligence Test;
therefore, pre and posttest statistical examinations were
not successful. However, considering all students were
able to successfully complete the second administration
of the SPM Fluid Intelligence Test, and there were no
statistical signicant differences in the demographic vari-
ables of the children in both groups, the research team was
comfortable using the second administration of the SPM
Fluid Intelligence Test in isolation in the current study
(see Results for further interpretation of this limitation).
Academic Achievement
The Palmetto Achievement Challenge Tests (PACT) is a
standards-based accountability measurement of student
achievement in 4 core academic areas—English/language
arts (ELA), mathematics, science, and social studies. The
PACT items are aligned to the South Carolina curriculum
standards developed for each discipline. An account-
ability system and a statewide test, such as the PACT,
are mandated by the South Carolina Education Account-
ability Act of 1998 and the federal No Child Left Behind
Act of 2001.30 PACT was administered approximately 1
week following the last integrative lesson on the nal
day of the intervention. Experimental and Control Group
children were simultaneously administered PACT. The
South Carolina Department of Education contracts with
an experienced company to print, distribute, scan, score,
346 Reed et al
and report PACT test results. Computer programming is
used to score the multiple-choice questions, and trained
professionals score students’ constructed-response and
extended writing.30 These scores were not returned to
this particular school district until the summer of 2008.
PACT testing does not begin until 3rd grade in South
Carolina, preventing the researchers from making pretest
comparisons.
Teacher Training
Teachers (n = 3) randomly assigned to the Experimental
Group received 2 training sessions before beginning
the current study and received 2 additional training
sessions during the study. Each training session lasted
approximately 90 minutes and focused on teaching math,
language arts and social studies with basic fundamental
locomotor skills (ie, hopping, skipping, jumping, run-
ning, etc). Teachers assigned to the Experimental Group
were provided food and refreshments during the training
sessions.
Body Mass Index Measures
Body Mass Index (BMI) was calculated using Fitness-
gram. This BMI protocol consisted of measuring height
and weight of each child. These measures were admin-
istered by the physical education teacher at the onset of
the study. These numbers were entered into the software
for Fitnessgram and a BMI value for each child was cal-
culated. This value is based on an appropriate body com-
position for specic weight and height and was derived
during physical education. Fitnessgram uses Healthy
Fitness Zones (HFZ) to evaluate tness performance
and were established by the Cooper Institute of Dallas,
Texas. Students in the current study were classied as:
a) in the Healthy Fitness Zone or b) not in the Healthy
Fitness Zone (non-HFZ) based on Fitnessgram’s specic
cut point classications for BMI.
Data Analysis
Descriptive statistics were used to examine frequencies
and percentage differences for all elements of the SPM
Fluid Intelligence Test, academic achievement (ie, PACT)
along with BMI classication. T-tests were used to exam-
ine mean differences between PACT scores adjusted by
group. In addition, Multivariate Analysis (MANOVA)
statistical models were used to examine differences
among results of Fluid Intelligence, academic achieve-
ment and BMI by group. The Statistical Package for the
Social Sciences (SPSS) 17.0 was used to analyze the data.
Results
Description of Experimental
and Control Populations
The Experimental and Control Groups had average ages
of 9.42 and 9.50, respectively. Approximately 43% and
44% of the Experimental Group and Control Group
children were females, respectively. Ninety-one percent
of children (n = 73) in the Experimental Group had a
BMI value, based on Fitnessgram cut points, in the HFZ
compared with 75% (n = 56) of Control Group children;
however, this difference was not signicant (P = .122).
No signicant differences, when adjusted for Ethnicity,
were found between groups (P > .05). Frequency and
percentage descriptive statistics for both the Experimental
and Control Groups by Ethnicity, Gender, BMI, and Age
are found in Table 1.
Children in the Experimental Group averaged (m
= 1,146) steps each integration day with a (SD = 356).
Each integrative lesson averaged approximately (m =
31 minutes) with a (SD = 4.58). Third grade children in
the Experimental Group averaged a cumulative cogni-
tive score on the SPM Fluid Intelligence Test of 38.61
postintervention. This gure was signicantly higher
than children in the Control Group (m = 36.66; P = .045
[multivariate statistics will be presented later in the text]).
Means and standard deviations for all 5 sections of the
SPM Fluid Intelligence Test with the Total scores for both
groups are found in Table 2.
The Previous Day Physical Activity Recall (PDPAR)
was administered in the beginning of the study in late Jan-
uary and during the last week in mid-April. No signicant
differences were found between both groups at the initial
administration (1st PDPAR Administration: Experimental
Group Mean = 29; Control Group Mean = 31) as well as
the second administration (2nd PDPAR Administration:
Experimental Group Mean = 49; Control Group Mean
= 49) of the PDPAR. Although, both groups increased
their physical activity outside of school from the rst
administration of the PDPAR to the second, this was most
likely due to seasonal changes. The initial administration
was conducted in late January before Daylight Savings.
The second administration of the PDPAR was completed
in mid-April where there was more sunlight during the
after school hours. Although, this hypothesis is based on
speculation-the PDPAR measures physical activity after
school and therefore it is reasonable to assume that young
children would not be allowed to play outside during
the dark during late January. Regardless of the increases
for both groups, there were no signicance differences
between groups at both PDPAR administrations (P > .05).
Multivariate analyses (MANOVA) revealed no
signicant Main Effect difference preintervention for
the Experimental and Control Groups by Gender, BMI,
Ethnicity, and Age (Pillai’s Trace = 0.035, F = 1.020; df-4,
140, P = .408). Similarly, no Between-Subjects Effects
were found between Experimental and Control Group
children for these variables. Therefore, the researchers
felt comfortable with the randomization of the classes
and students in the current study. Furthermore, since
no signicant differences were observed between the
independent variables, the researchers were condent in
making comparisons between groups for the SPM Fluid
Intelligence Test and achievement tests (ie, PACT) in the
current study.
When all of the data were analyzed regardless of
group classication, some interesting patterns emerged
Fluid Intelligence and Academic Performance 347
Table 1 Frequency and Percentages of Demographic Variables by Group
Variable N Ethnicity Gender BMI Age
Exp. Group 80 White = 75 (94%)
Other = 5 (6%)
Female = 34 (42.5%)
Male = 46 (57.5%)
HFZ = 73 (91.2%)
Not HFZ = 7 (8.8%)
9 = 47 (58.8%)
10 = 32 (40%)
11 = 1 (1.2%)
Control
Group
75 White = 68 (90.7%)
Other = 7 (9.3%)
Female = 33 (44%)
Male = 42 (56%)
HFZ = 56 (74.7%)
Not HFZ = 19 (25.3%)
9 = 38 (50.7%)
10 = 36 (48%)
11 = 1 (1.3%)
Table 2 Means and Standard Deviations of Fluid Intelligence
Scores
by Group
Group Fluid I. Test N Mean SD
Experimental
A 80 10.43 1.11
B 80 9.94 2.14
C 80 7.25 2.06
D 80 8.20 1.88
E 80 2.75 1.88
Total 80 38.60* 6.13
Control
A 75 10.47 1.04
B 75 9.82 1.99
C 75 6.72 2.21
D 75 7.57 2.76
E 75 2.08 1.49
Total 75 36.66 6.40
* Signicance at the 0.05 level.
for the SPM Fluid Intelligence Tests. One-hundred and
eleven (n = 111) 3rd grade children were considered a
healthy weight based on Fitnessgram’s cut points for
the HFZ. In contrast, (n = 26) children were classied
as having an unhealthy body composition and non-HFZ.
Eighteen children in the current study did not attend
school at the time of BMI classication and therefore
were not included in the analysis as a result of not receiv-
ing a BMI score. Children considered a healthy weight
based on Fitnessgram’s classication for HFZ, earned
higher scores on all components of the SPM Fluid Intel-
ligence Test in comparison with their nonhealthy weight
(non-HFZ) peers. Although only one of the components
was signicantly higher between the 2 BMI classica-
tion groups (SPM Section C, F = 7.638, P = .007) the
total score neared signicance (Pillai’s Trace = 0.084, F
= 1.989; P = .072). The means and standard deviations
for the entire sample by BMI classication (HFZ vs.
non-HFZ) of the SPM Fluid Intelligence Test are listed
in Table 3.
Examination of the PACT scores by group revealed
interesting results. Signicant differences (t test for equal-
ity of means = 2.936, P = .004) between the Experimental
and Control Groups in Social Studies was found. Chil-
dren in the Experimental Group had a greater percent-
age receive a Procient and Advanced designation than
children in the Control Group. Approximately 82% of
children in the Experimental Group earned a Procient or
Advanced designation on the Social Studies PACT com-
pared with only 60.9% of children in the Control Group.
No signicant differences between the Experimental
and Control Groups (t test for equality of means = 1.107,
P = .09.) on the Math PACT were observed. Yet, children
in the Experimental Group had a greater percentage of
children receive a Procient and Advanced designation
than children in the Control Group. Approximately 49%
of children in the Experimental Group earned a Procient
or Advanced designation on the Math PACT compared
with 34.7% of children in the Control Group.
Similarly, no signicant difference between the
groups on English/Language Arts PACT (t test for equal-
ity of means = .711, P = .478) was found. Children in
the Experimental Group, however, did have a greater
percentage of children receive a Procient and Advanced
designation then children in the Control Group. Approxi-
mately 82% of children in the Experimental Group earned
a Procient or Advanced on the English/Language Arts
PACT compared with 75.3% of children in the Control
Group.
No signicant difference between the groups on the
Science PACT (t test for equality of means = 1.490 P =
.140) was found as well. No child in the Experimental
348 Reed et al
Group scored Below Basic on the Science PACT. Chil-
dren in the Experimental Group had a greater percentage
of children receive a Procient and Advanced designation
then children in the Control Group. Approximately 80%
of children in the Experimental Group earned a Procient
or Advanced designation on Science PACT compared
with 72.2% of children in the Control Group. In addition,
41% of children in Experimental Group received a score
of Advanced compared with only 25% of Control Group
children. Frequencies, percentages and t test values of
PACT items by Group are listed in Table 4.
Examination of the PACT scores adjusted for BMI
classication (ie, HFZ vs. non-HFZ) revealed similar
results to PACT scores adjusted by Group Classication
(ie, Experimental vs. Control). Children classied in the
non-HFZ received signicantly lower scores (F = 5.932,
df-1, 78, P = .017) on the Social Studies PACT than chil-
dren classied in the HFZ BMI. Although, no signicant
differences were found between children on the other 3
PACT (English/Language Arts, Math and Science) tests
adjusted by HFZ and non-HFZ, HFZ children received
a greater percentage of Advanced and Procient scores
than their non-HFZ peers.
Conclusions
Children in the Experimental Group averaged close to
1,200 pedometer steps per day, thus averaging 3,600 steps
per week during the classroom-based movement activi-
ties. This is an extremely important value since experts
recommend that a 30- minute physical education class
should provide children with an opportunity to accumu-
late 1200 to 2000 steps.31 The fact that the integrative
movement was performed in a classroom setting with
spatial constraints not observed in a gymnasium, and that
the classroom teacher was not trained to teach physical
education, suggests how impressive the daily and weekly
step totals were. It is readily apparent that children and
adolescents based on data presented in the Introduction
of this practicum paper are not participating in the recom-
mended levels of physical activity contributing to a host
health problems including childhood obesity. Perhaps,
integrating movement regularly into the classroom can
serve to reduce the risk associated with this growing
epidemic.
Furthermore, previous research germane to this study
has documented that studies on science32 and language
arts have illustrated benecial effects for integrating
these disciplines with physical activity. An integrative
curriculum provides students with a global view of learn-
ing and can teach skills necessary for the transference of
knowledge gained in one area into another.33 Both teach-
ers and students benet from interdisciplinary learning
as it builds an understanding of other subject areas and
teaching methods.34,46 Daryl Siedentop35 a famed physi-
cal educator from The Ohio State University, posits that
students learn through their involvement with the content.
Integration of subject matter allows for more student
involvement in the learning experiences.36
As the data in the Results section illustrates, sig-
nicant differences between Experimental and Control
Group children were found on some of the SPM Fluid
Intelligence Test components. These ndings conrmed
what was presented in the Introduction, and are reafrmed
by Lochbaum and colleagues37 examining the relation-
ship between exercise training history and performance
on uid intelligence. Results from their study found that
aerobically trained or physically active participants per-
formed signicantly better on the uid intelligence task
than untrained or inactive participants. A more recent
study published in the American Journal of Public Health
by Singh-Manoux and colleagues38 examining the impact
of physical activity on cognitive function of middle-age
Table 3 Means and Standard Deviations of Fluid Intelligence Items by BMI
BMI Fluid I. Test N Mean SD
HFZ A 111 10.47 1.05
B 111 9.94 2.13
C 111 7.18* 2.13
D 111 7.97 2.31
E 111 2.49 1.77
Total 111 38.02 6.42
Non-HFZ A 26 10.38 1.23
B 26 9.73 1.92
C 26 5.92 1.93
D 26 7.50 2.70
E 26 1.96 1.39
Total 26 35.73 5.90
* F = 7.638, P = .007.
Fluid Intelligence and Academic Performance 349
individuals revealed that low levels of physical activity
was identied as a risk factor for poor performance on
uid intelligence tasks. The current investigation provides
further evidence that movement can positively inuence
uid intelligence of youth, and should be considered an
essential element to promote cognitive development of
elementary-age children in a public school setting.
Although this evidence is worth noting, what was
equally compelling were the differences in SPM Fluid
Intelligence Test scores for children who were distin-
guished, based on Fitnessgram’s BMI cut points for
meeting and not meeting the HFZ and non-HFZ. It was
apparent that when the entire sample was examined,
children who did not meet the requirements for a healthy
BMI based on Fitnessgram cut points earned lower scores
on many of the SPM Fluid Intelligence individual com-
ponents and/or the Total Score. This nding is similar
to the research by Dr. Davis from the Medical College
of Georgia. Dr. Davis and colleagues39 tested the effect
of aerobic training on executive function in overweight
children. Executive function tends to correlate with uid
intelligence and is an appropriate comparison for the cur-
rent study. Fluid intelligence, similar to executive func-
tion, is related to planning and organizing information,
and was related to physical activity in their study. Dr.
Davis and colleagues39 found that children who received
the high-dose of physical activity had higher planning
scores than the controls. Exercise, according to these
researchers, may be a simple but important method to
enhance mental function.39
The executive function hypothesis originated in the
eld of gerontology40–42 and is based on the idea that the
largest improvements in cognition due to exercise and
physical activity are on the ability to plan, initiate and
carry-out activity sequences that comprise goal-directed
behavior.39 Regular exercise may be a simple, important
method of enhancing children’s cognitive and academic
development considering that, according to Welch and
colleagues43 executive function begins during early child-
hood and extends through adolescence.
Additional benets linked to physical activity and
learning was recently disseminated in a published review
paper. Taras44 revealed that physical activity improved
concentration, along with reading and mathematic per-
formance, with the strongest relationship between activity
and concentration. Physical activity has also been known
to stimulate the release of epinephrine and norepinephrine
(adrenalin) enabling children to become alert and ready
to learn.8,9,44
A recent brief from Active Living Research Pro-
gram Ofce sponsored by the Robert Wood Johnson
Foundation further validates the impact of movement
on academic achievement and performance. This brief
provides empirically based data that concludes the fol-
lowing: Sacricing physical education for classroom time
does not improve academic performance. Youth who are
more physically active tend to perform better academi-
cally. Kids who are physically active and t are likely to
have stronger academic performance. Activity breaks can
improve cognitive performance and classroom behavior.
Short activity breaks during the school day can improve
students’ concentration skills and classroom behavior.45
Limitations
The primary limitation in the current study was not
including pretest SPM Fluid Intelligence Data. How-
ever, as previously mentioned, Multivariate analyses
(MANOVA) revealed no signicant main effect differ-
ence preintervention for the Experimental and Control
Groups by Gender, BMI, Ethnicity, and Age. Similarly,
no Between-Subjects Effects were found between Experi-
mental and Control Group children for these variables.
Therefore, the randomization of the classes and students
Table 4 Frequencies, Percentages, and t-test Values of PACT Scores by Group
Group
PACT
LEVEL
Eng/L. Arts (%)
t
-test for equality
of means = .711,
P
= .478
Math (%)
t
-test for equality
of means = 1.107,
P
= .09
Soc. Stud. (%)
t
-test for equality
of means = 2.936,
P
= .004
Science (%)
t
-test for equality
of means = 1.490,
P
= .140
Experimental Below Basic
Basic
Procient
Advanced
1 (1.3%)
13 (16.7%)
45 (57.7%)
19 (24.4%)
4 (5.1%)
36 (46.2%)
24 (30.8%)
14 (17.9%)
0 (0.00%)
6 (17.6%)
6 (17.6%)
22 (64.7%)
0 (0.00%)
9 (20.5%)
17 (38.6%)
18 (40.9%)
Control Below Basic
Basic
Procient
Advanced
0 (0.00%)
17 (24.6%)
37 (53.6%)
15 (21.7%)
5 (6.9%)
42 (58.3%)
17 (23.6%)
8 (11.1%)
10 (21.7%
8 (17.4%)
8 (17.4%))
20 (43.5%)
1 (2.8%)
9 (25.0%)
17 (47.2%)
9 (25.0%)
Note. Advanced = the student exceeded expectations for student performance based on the curriculum standards. Procient = the student has met
expectations for student performance based on the curriculum standards. Basic = the student has met minimum expectations for student performance
based on the curriculum standards. Below Basic = the student has not met minimum expectations for student performance based on the curriculum
Standards.30
350 Reed et al
in the current study was appropriate. Furthermore, since
no signicant differences were observed between the
independent variables, the researchers were condent
in making comparisons between groups for the SPM
Fluid Intelligence Test and achievement tests (ie, PACT).
Finally, because PACT does not begin until 3rd grade in
South Carolina, this limitation prevented the research
team from conducting prepost test comparisons in the
current study.
Implications
The primary implication arising from the current study is
to offer training for elementary school teachers on how
to integrate physical activity in the classroom. Integrat-
ing physical activity will not only help to teach complex
information to varying children with differing learning
needs, but it might also help to intervene on the risky
behavior of inactivity and increased likelihood of child-
hood obesity. Evidence from the current study indicates
that integrating movement in the classroom 3 days per
week for an average of 90 minutes total per week can
enhance uid intelligence and select academic achieve-
ment scores of elementary-age children, but further
studies are needed to conrm these preliminary ndings.
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