THE EFFECT OF AN ACUTE BOUT OF PHYSICAL ACTIVITY ON INHIBITORY
CONTROL IN INDIVIDUALS WITH AUTISM SPECTRUM DISORDER
By
Andrew C. Parks

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Kinesiology – Doctor of Philosophy
2017

PUBLIC ABSTRACT
THE EFFECT OF AN ACUTE BOUT OF PHYSICAL ACTIVITY ON INHIBITORY
CONTROL IN INDIVIDUALS WITH AUTISM SPECTRUM DISORDER.
By
Andrew C. Parks
Given the growing prevalence of autism spectrum disorder (ASD) in the U.S., many
researchers have dedicated their work to improving the quality of life for this population through
the reduction in ASD symptoms. Interestingly, a growing body of research has suggested these
symptoms may be related to aspects of cognitive function that have been shown to be affected by
short-bouts of physical activity. Therefore, this study sought to explore the effects of short
duration physical activity on cognitive function in individuals with ASD. For this study, 18
individuals with ASD and 18 typically developing (TD) individuals completed a computer task
to assess cognitive function before and after either walking on a treadmill or sitting while
reading. Findings from the study indicated that participants with ASD had poorer response
accuracy to the task when compared to their TD peers. Additionally, prior to the walking
condition, participants with ASD responded slower on average when compared to their
performance prior to the reading condition. Although there is evidence supporting a difference
between those with ASD and their TD peers in cognitive function, it is still unclear what role
physical activity may play in addressing this difference. However, this study does provide an
initial foundation for future research in this area by providing insight for study designs and
feasibility in this area of research.

ABSTRACT
THE EFFECT OF AN ACUTE BOUT OF PHYSICAL ACTIVITY ON INHIBITORY
CONTROL IN INDIVIDUALS WITH AUTISM SPECTRUM DISORDER.
By
Andrew C. Parks
Concomitant with the increased prevalence of autism spectrum disorder (ASD) over the
last two decades, interest in enhancing individual quality of life for those diagnosed by reducing
ASD-related symptomologies has grown. Although evidence has suggested these
symptomologies may be linked with deficits in inhibitory control and a growing body of
literature has indicated benefits in inhibitory control associated with acute physical activity, the
extent to which physical activity may influence inhibition in individuals with ASD is not well
understood. Accordingly, the aim of this investigation was to examine the effects of an acute
bout of aerobic physical activity on task performance indices of inhibitory control in individuals
with ASD. Using a within-subjects crossover design, 18 individuals with ASD and 18 typically
developing individuals were assessed for differences in task performance (reaction time and
response accuracy) in response to a modified Eriksen flanker task prior to and 10-minutes
following a 20-minute bout of aerobic exercise or seated reading across multiple,
counterbalanced, sessions. Results showed a significant difference between groups for overall
response accuracy with individuals with ASD displaying poorer response accuracy. Slower
reaction time was also observed between rest and exercise conditions at pretest, specific to the
ASD group. No significant differences were observed, however, at posttest or from pre- to
posttest for either group based on mode. Despite evidence supporting differences between
groups based on task performance, it remains unclear if, and to what degree, physical activity
may influence interference control in individuals diagnosed with ASD. However, the findings of

this study do provide evidence for feasibility and insight regarding study design, establishing the
framework for future research.

Copyright by
ANDREW C. PARKS
2017

I would like to dedicate this dissertation to my mother and sister. I owe much of who I
am today to the two of you. Mom – You have consistently displayed hard work, dedication, and
compassion in both your personal and professional lives. While you set the bar high, your
example has given me something to consistently strive for. I consider myself lucky to have had
such a wonderful role model, and privileged to have built the relationship we share today.
Meghan – Seeing the passion you have for your work and the influence you have on your
students’ lives is truly inspiring. I hope my work can one day effect the lives of others the way
you have affected so many. Thank you both for the constant support, encouragement, and
shoulder to lean on. I could not have done this without either of you.

v

ACKNOWLEDGEMENTS

Throughout my academic studies, I have been incredibly fortunate to have a remarkable
support system to not only guide me, but to help mold me into the researcher, academic, and
person I am today. I will forever be indebted to you all, and will never take for granted the
investment you have all made in me.
Matt: Just about six years ago, I sat down for breakfast on the doctoral visitation and my entire
world changed. It didn’t take long into my conversation with you that I knew I wanted to work
in your lab, and I will never be able to truly express my thanks to you for taking a chance on me.
I know I have not always been the easiest student to mentor, but you have always been patient
with me and pushed me when I needed it. I have learned more from you in the last five years
about being a good researcher and mentor, than I could have ever imagined. Thank you.
To my committee: It has been a long road to get to this point, but your belief that I could, and
would, complete this work never wavered (even if mine did at times). You constantly kept me
focused on the task at hand, and even throughout rough recruitment periods you always had a
positive thought to keep me going. Dr. Smith – thank you, and your lab, for embracing me and
my research interests so readily. Your ability to help broaden my perspective from the
laboratory to the real-world has made me a better researcher, and I will always be grateful for the
new lens in which I see my work. Dr. Hauck – given how small the physical activity and ASD
research community is, it was a blessing to have you join the department during my time at
MSU. Your guidance in working with this population has been exceptional, but your faith that I
will be a good advocate for these individuals and their families has meant the world to me. Dr.
Ingersoll – almost 4 years ago when Matt and I brought this idea to you, it would have been easy

vi

to see the challenges this study would impose and send us right back out your door. But, instead
you helped to open a door to a whole new area of research for myself and my discipline, for
which I would like to express my sincerest thank you.
To my lab mates: We have been through a lot together, that I don’t think anyone outside the
HBCL will ever truly understand. Katy – from the moment you joined the lab as an RA, I knew
you were a special addition to the lab. Through your help with data collection during undergrad,
to your collaboration in the lab as a colleague, you have always had my back and continuously
push me to be a better researcher, colleague, and friend. Amanda – During your visit to MSU, I
told Matt that of every person we had interviewed in the four years I had been here your
personality was by far the best fit, and I am so glad to have been proven right. You not only
came into the lab ready to go, but you never blinked an eye whenever I have asked for help.
Anthony – who would have thought that a fast-talking guy from New Jersey, and this
Midwesterner would ever become friends. Through the early mornings in the lab, the nights out
in EL, and the late-night entourage parties, you have always been there when I’ve needed it. It’s
crazy to think that this chapter of our friendship will be over soon, but I look forward to what the
next one will bring. I will always be grateful to you three, and cannot wait to see what the future
holds for all of us.
To my family: Of all the people I want to thank, you all deserve it the most. These last eleven
years, from undergrad to grad school, have not always been the easiest for me, and no matter
what I always knew I had someone on the other end of the phone that I could call. Whether it
was to talk about something exciting or to vent about a bad day, there was always someone
willing to listen. Mom & Joel – Your constant support has made me feel so privileged to call
you both my parents. Throughout this entire journey, you have always helped keep me grounded

vii

by reminding me that I love the work I do, and that my work matters even when it seemed there
was no end in sight. Meghan & Dave – Your work as teachers has been inspiring and I could not
ask for better role models. Through the nights of CoD, to the randomly absurd card in the mail,
you have both helped me find bright spots in many of my not so bright days. While I know I
could never repay what you all have done for me, I hope to always make you proud.

viii

TABLE OF CONTENTS

LIST OF TABLES ................................................................................................................... xi
LIST OF FIGURES ................................................................................................................ xii
KEY TO ABBREVIATIONS ................................................................................................ xiii
CHAPTER 1 ..............................................................................................................................1
Introduction ................................................................................................................................1
CHAPTER 2 ..............................................................................................................................6
Review of Literature ..................................................................................................................6
Autism Spectrum Disorder ............................................................................................6
Cognitive Control...........................................................................................................9
Cognitive Control Characteristics in Individuals with ASD........................................11
Physical Activity Trends in Individuals with ASD......................................................13
Acute Physical Activity Influences on Physical and Cognitive Health ...................... 18
Purpose.........................................................................................................................22
Rationale ......................................................................................................................23
Hypotheses ...................................................................................................................23
CHAPTER 3 ............................................................................................................................25
Methodology ............................................................................................................................25
Participants and Recruitment .......................................................................................25
Exclusionary criteria. .......................................................................................26
Power Analysis ............................................................................................................26
Cognitive Control Task ................................................................................................27
Experimental Conditions .............................................................................................28
Procedure .....................................................................................................................29
Statistical Analysis .......................................................................................................31
CHAPTER 4 ............................................................................................................................33
Results ......................................................................................................................................33
Participant Characteristics ...........................................................................................33
Task Performance ........................................................................................................33
Reaction time ...................................................................................................33
Response accuracy ...........................................................................................34
Interference scores ...........................................................................................34
Change scores ..................................................................................................34

CHAPTER 5 ............................................................................................................................35
Discussion ................................................................................................................................35

ix

Task Performance ........................................................................................................36
Flanker task check............................................................................................36
Reaction time ...................................................................................................36
Response accuracy ...........................................................................................37
Interference & change scores ...........................................................................39
Practical Implications...................................................................................................40
Limitations & Future Directions ..................................................................................42
Conclusion ...................................................................................................................45
APPENDICES .........................................................................................................................47
Appendix A: IRB Approval Letter ..............................................................................48
Appendix B: Dissertation Funding Sources .................................................................49
Appendix C: Informed Assent – Age 5-7 ....................................................................50
Appendix D: Informed Assent – Age 8-12 ..................................................................51
Appendix E: Informed Assent – Age 13-17 ................................................................53
Appendix F: Informed Consent – Age 18+ .................................................................56
Appendix G: Informed Consent – Parent.....................................................................59
Appendix H: Recruitment Flyer for Individuals with ASD .........................................63
Appendix I: Recruitment Email for Individuals with ASD .........................................64
Appendix J: Recruitment Flyer for TD Individuals .....................................................65
Appendix K: Recruitment Email for TD Individuals ..................................................66
Appendix L: SNAP-IV ................................................................................................67
Appendix M: Social Communication Questionnaire (SCQ) .......................................69
Appendix N: Physical Activity Readiness Questionnaire (PAR-Q) ............................71
Appendix O: Health History Demographic Survey .....................................................72
Appendix P: Tables for Results Section ......................................................................89
Appendix O: Figures for Results Section ....................................................................93
REFERENCES ........................................................................................................................99

x

LIST OF TABLES

Table 3.1. Inclusion Criteria for Participant Acceptance into the Current Project ..................27
Table 6.1. Participant demographic values (Mean ± SD) ........................................................89
Table 6.2. Ranges for participant demographics .....................................................................90
Table 6.3. Clinical status confirmation for the ASD group (Mean ± SD) ...............................91
Table 6.4. Mean (± SD) Task Performance Characteristics ....................................................92

xi

LIST OF FIGURES

Figure 3.1. Illustration of the congruent (A) and incongruent (B) goldfish stimuli used in the
modified flanker task. ............................................................................................29
Figure 6.1. Copy of IRB approval letter ..................................................................................48
Figure 6.2. Informed assent paperwork for children 5 to 7 years old ......................................50
Figure 6.3. Informed assent paperwork for children 8 to 12 years old ....................................51
Figure 6.4. Informed assent paperwork for children 13 to 17 years old ..................................53
Figure 6.5. Informed consent paperwork for adults 18 years old and older ............................56
Figure 6.6. Informed consent paperwork for parents of children under 18 years old ..............59
Figure 6.7. Recruitment flyer for individuals with ASD .........................................................63
Figure 6.8. Recruitment flyer for typically developing individuals.........................................65
Figure 6.9. SNAP-IV assessment for expression of ADHD symptoms ..................................67
Figure 6.10. Social communication questionnaire assessment ................................................69
Figure 6.11. Physical activity readiness questionnaire ............................................................71
Figure 6.12. Health history demographic form completed online ...........................................72
Figure 6.13. Mean HR (± SE) for each group across experimental condition.........................93
Figure 6.14. Mean (± SE) RT latency for (A) congruent and (B) incongruent trials for each
condition by group .................................................................................................94
Figure 6.15. Mean (± SE) response accuracy for (A) congruent and (B) incongruent trials for
each condition by group ........................................................................................95
Figure 6.16. Mean (± SE) interference score for (A) reaction time latency and (B) response
accuracy for each condition by group ...................................................................96
Figure 6.17. Dot plots for reaction time latency at pretest, posttest, and change score for each
condition by group .................................................................................................97
Figure 6.18. Dot plots for response accuracy at pretest, posttest, and change score for each
condition by group .................................................................................................98
xii

KEY TO ABBREVIATIONS

ADHD

Attention Deficit/Hyperactivity Disorder

ADOS-2

Autism Diagnostic Observation Schedule

ANOVA

Analysis of Variance

ASD

Autism Spectrum Disorder

CDC

Centers for Disease Control and Prevention

DSM-5

Diagnostic and Statistical Manual of Mental Disorders – 5th edition

HHD

Health History Demographic

HR

Heart Rate

NCLB

No Child Left Behind Act of 2001

PAR-Q

Physical Activity Readiness Questionnaire

RT

Reaction time

SCQ

Social Communication Questionnaire

SES

Socioeconomic Status

SNAP-IV

Swanson, Nolan, and Pelham Questionnaire – 4th edition

TD

Typically Developing

VO2

Aerobic Capacity

VO2max

Maximum Aerobic Capacity

WASI-II

Wechsler Abbreviated Scale of Intelligence – 2nd edition

xiii

CHAPTER 1
Introduction
Recent estimates from the Autism and Developmental Disabilities Monitoring Network
suggest that 1 in 68 children are diagnosed with an Autism Spectrum Disorder (ASD; Baio,
2014; Christensen et al., 2016). This pervasive developmental disorder is often identified by
impairments in social and communicative interaction, and restrictive or repetitive behaviors
(American Psychiatric Association, 2013). While the etiology of this disorder is still unclear and
there exists a wide spectrum of symptom expression (Volkmar, Cook, Pomeroy, Realmuto, &
Tanguay, 1999), evidence suggests a possible link between the underlying symptomologies of
ASD and deficits in cognitive function — particularly in areas related to cognitive control (Hill,
2004; Hughes, Russell, & Robbins, 1994; Ozonoff & Jensen, 1999). Interestingly, such
impairments in cognition mirror those aspects of cognition that are enhanced following a bout of
physical activity. Specifically, a growing body of evidence in both typically and atypically
developing children has observed that 20-minutes of moderate intensity aerobic physical activity
can improve behavioral task performance in response to cognitive control related tasks (Drollette
et al., 2014; Drollette, Shishido, Pontifex, & Hillman, 2012; Hillman, Pontifex, et al., 2009;
Pontifex, Saliba, Raine, Picchietti, & Hillman, 2013). Thus, given that individuals with ASD
exhibit core deficits in the same aspects of cognition that are enhanced following participation in
a single bout of physical activity, investigation of the extent to which a single dose of physical
activity may serve to influence the cognition of individuals with ASD may inform evidence
based recommendations for clinical practice and educational policy within this population.
Cognitive control — a term used synonymously with executive function — refers to a set
of computational processes involved in the scheduling, selection, maintenance, and coordination

1

of high-order cognitive functions (Hillman, Erickson, & Kramer, 2008; Pontifex et al., 2011;
Rogers & Monsell, 1995). These processes regulate an individual’s goal-directed interactions
with the environment (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Voss et al., 2011).
Cognitive control is collectively comprised of three core cognitive processes: working memory,
cognitive flexibility, and inhibition (Diamond, 2012; Miyake et al., 2000). Of these core
processes, evidence suggests that individual’s with ASD, children in particular, exhibit distinct
impairments related to inhibition (Christ, Kester, Bodner, & Miles, 2011; Kana, Keller,
Minshew, & Just, 2007). This component process has been identified as particularly important
within developing populations as it relates to the ability to suppress a pre-potent actionschema/override an on-going response as well as gate out task irrelevant environmental
information (Barkley, 1997; Davidson, Amso, Anderson, & Diamond, 2006). Interestingly,
evidence suggests that the impairment in inhibition evident in children with ASD appears to be
specific to the interference component of inhibition (Adams & Jarrold, 2012; Christ, Holt, White,
& Green, 2007; Keehn, Lincoln, Müller, & Townsend, 2010). That is, Adams and Jarrold (2012),
employed a sample of 15 children with diagnosed ASD and 15 match-control typically
developing children to specifically test the extent to which ASD-related impairments in
inhibition generalized across inhibitory control domains. In response to a modified flanker task,
children with ASD demonstrated poorer response accuracy when interference control demands
were the greatest, relative to the match-control children; whereas no differences between groups
were observed relative to performance on a stop-signal task which provided an index of
inhibiting of action (Adams & Jarrold, 2012). These findings, among others (Adams & Jarrold,
2009; Lopez, Lincoln, Ozonoff, & Lai, 2005; Ozonoff & Jensen, 1999), have suggested that the
deficits in inhibitory control for children with ASD do not generalize across inhibition domains,

2

but rather are specific to managing task irrelevant information. As complex social interactions
often rely on subtle social and physical cues amid a multitude of other potentially irrelevant
factors (Hanley et al., 2014), this deficit in managing interference to focus on relevant indicators
may in part underlie ASD related symptomologies in social interactions. With research
indicating enhancements in interference control following a single bout of physical activity
(Drollette et al., 2014; Hillman, Pontifex, et al., 2009; Pontifex et al., 2013), such that
preadolescent children are able to more effectively distinguish relevant task information amongst
a set of irrelevant stimuli, physical activity may be a means to reduce symptomologies related to
social impairments in children with ASD. Acute bouts of physical activity have been shown to
have a beneficial influence on interference control (Hillman et al., 2006; Hillman, Pontifex, et
al., 2009; Pontifex & Hillman, 2007), with research indicating that a bout of aerobic physical
activity lasting at least 20-minutes can enhance performance on tasks requiring aspects of
interference control (Drollette et al., 2014; Hillman, Pontifex, et al., 2009; Pontifex et al., 2013).
Specifically, in an initial investigation of the influence of a single bout of physical activity on
interference aspects of inhibitory control in preadolescent children, Hillman and colleagues
(2009) observed that following a single 20-minute bout of moderate-intensity physical activity,
children demonstrated greater performance relative to following a similar duration of seated
reading. Such bouts of moderate intensity aerobic physical activity have also been found to
enhance interference related aspects of inhibitory control in children with Attention
Deficit/Hyperactivity Disorder (ADHD). Utilizing a sample of 20 preadolescent children
suspected or diagnosed with ADHD, and 20 match-control children, Pontifex and colleagues
(2013) assessed the effect of a 20-minute aerobic exercise condition relative to 20-minutes of
seated reading on behavioral performance in response to a modified flanker task. Findings from

3

this investigation indicated that all participants experienced benefits as indexed by greater
response accuracy following acute exercise relative to reading, with those diagnosed with ADHD
also experiencing additional physical activity related enhancements in regulatory adjustments in
behavior as indexed by greater slowing of reaction time on trials immediately following an
erroneous response. Such generalized enhancements, with selective benefits for children with
ADHD, taken together with the high comorbidity/dual diagnosis associated with ADHD and
ASD (American Psychiatric Association, 2013), suggest that these bouts of physical activity may
provide a means of enhancing interference control in children with ASD.
Despite evidence indicating a positive effect of a bout of physical activity on interference
control in preadolescent populations, an overarching limitation of this literature base to date is
the experimental designs used to determine the effect of physical activity on cognition
(Lambourne & Tomporowski, 2010). That is, evidence in this area is drawn from studies which
have, to-date, relied on within-subjects experimental designs which assessed differences in
cognition following exercise relative to following a seated control condition with the
experimental conditions occurring on separate days (Hillman, Pontifex, et al., 2009; Pontifex et
al., 2013). Such designs are potentially problematic given evidence for day-to-day variations in
neurological components associated with attentional processing (Polich & Kok, 1995), thus such
differences may manifest independent of the experimental conditions. Alternatively, other
investigations in this area have relied upon between-subject’s experimental designs in which
participants engage in cognitive testing prior to, and following an acute bout of exercise (Ferris,
Williams, & Shen, 2007; Magnié et al., 2000; Nakamura, Nishimoto, Akamatu, Takahashi, &
Maruyama, 1999; Yagi, Coburn, Estes, & Arruda, 1999). This method, unlike the previous, does
not include a non-exercise control group, with any changes in cognition assessed from pre-

4

exercise to post-exercise; rendering it difficult to distinguish effects related to exercise from
those associated with exposure to the task (Pontifex, Parks, Henning, & Kamijo, 2015).
Therefore, in effort to better assess the effects of physical activity within this study, a withinsubjects repeated measures design will be implemented utilizing both exercise and control
experimental conditions as well as pre-posttest assessments.

5

CHAPTER 2
Review of Literature
Autism Spectrum Disorder
Over the last two decades, Autism Spectrum Disorder (ASD) has been identified as one
of the fastest growing developmental disorders in the United States, with a growing impact not
only on the quality of life for individuals diagnosed but also on family and caregivers who work
with them. To better understand the effects of ASD on not only the individuals diagnosed with
the disorder, but also those who care for them, the Center for Disease Control and Prevention
(CDC) developed the Autism and Developmental Disabilities Monitoring Network in 2000
(Baio, 2014; Centers for Disease Control and Prevention, 2016; Christensen et al., 2016). This
network helped to elucidate the current prevalence of the disorder, with 1 in 68 children
diagnosed in the United States (U.S.), indicating an increase in the U.S. of 29% since 2008, 64%
since 2006, and 123% since 2002 (Baio, 2014). It is believed that this increase in prevalence is
due primarily to improved diagnostic criteria and awareness, as the cause for this disorder is still
unknown with a large number of cases identified as idiopathic (Schaaf & Zoghbi, 2011). In
accordance with this increase in diagnosis of ASD in the last decade, concerns as to how these
individuals may be assisted and treated has also grown. With many individuals receiving
assistance from caregivers and family members, estimated lifetime costs for those supporting an
individual with ASD can be as high as $2.4 million (Buescher, Cidav, Knapp, & Mandell, 2014).
Ranging from behavioral and speech therapies, to pharmacological treatments, these strategies
have placed a large financial and psychological demand on the individuals diagnosed with ASD
and their families. While the economic cost is high, it is also important to recognize the time
constraints and social pressures experienced by these individuals and their families, with a

6

number of researchers identifying that caregivers for individuals with ASD exhibit greater levels
of stress, anxiety, and depression when compared with parents of typically developing children
and those with other developmental disorders (Abbeduto et al., 2004; Hastings & Johnson, 2001;
L. E. Smith, Seltzer, Tager-Flusberg, Greenberg, & Carter, 2008). As such, interest in nonpharmaceutical strategies to address cognitive related issues associated with the disorder have
grown, with many focusing on ways to help with social interaction and suppression of
stereotypical behavioral patterns (Koenig et al., 2010).
Autism Spectrum Disorder (ASD) comprises a set of developmental disabilities identified
through the Diagnostic and Statistical Manual of Mental Disorders – 5th edition (DSM-5) by
impairments across two domains of functioning (American Psychiatric Association, 2013).
Identified as a pervasive disorder, individuals diagnosed with ASD exhibit deficits in restrictive
or repetitive patterns of behavior and/or interests, and social and communicative interactions
(American Psychiatric Association, 2013). Recently adopted by the DSM-5, the phrase
“spectrum disorder” is now used to reflect the wide range of behaviors and symptom expressions
associated with the disorder (Volkmar et al., 1999). In accordance with the myriad of ways in
which this disorder may present, it has become increasingly important to promote a greater
understanding of the disorder among the general populace in hopes of providing a more inclusive
and available community environment to those diagnosed with ASD and their families. One
setting in which this need has become more evident is within the classroom. For instance,
restrictive and repetitive behaviors (RRBs) may present in many ways (i.e., utterances, repetitive
use of objects, unusual or all-consuming interests, and compulsive, rigid, ritualistic behaviors),
each of which can affect not only the learning process for the child in the classroom (along with
learning process for their peers), but may also impact their social interaction with their peers.

7

Deficits associated with social interaction are commonly observed among individuals diagnosed
with ASD, and often present as behaviors such as: impaired social reciprocity, perspective
taking, and nonverbal communication; as well as difficulties building flexibly appropriate social
relationships (Williams White, Keonig, & Scahill, 2007). Much like the impairments associated
with RRBs, these deficits may also manifest in a variety of ways (i.e., failure to acquire any
speech, a need for alternative communication methods, use of stereotyped speech, echolalia,
and/or simply having difficulty following typical rules of conversation; American Psychiatric
Association, 2013), with each having the potential to substantially contribute to the challenges an
individual with ASD faces when attempting to assimilate into a classroom environment. While a
variety of treatment methods and behavioral strategies are implemented to help address these
deficits and help those with ASD in the classroom, the varying nature of how the disorder
presents with each diagnosis has made addressing these deficits increasingly difficult.
With concerns related to this disorder continuing to rise, many researchers have
attempted to address the underlying mechanisms related to this disorder, yet the etiology remains
elusive. Current suggestions for the causes of the disorder have involved many different avenues
including genetics (Huguet, Ey, & Bourgeron, 2013) and pharmacology (Christensen et al.,
2013; Strömland, Nordin, Miller, Akerström, & Gillberg, 1994), however due to the inability to
isolate a direct cause for the disorder many researchers have begun to focus on identifying the
underlying factors contributing to the disorder and ways to address them. Understanding that
each case of ASD is unique is important, as each treatment approach must be tailored to the
individual and family involved. Due to the complexities of the disorder and its currently
unknown etiology, these treatments should be considered a long-term commitment for the
individual to experience optimal results from the intervention, but not as a cure. For many, these

8

interventions mean working with a treatment team (i.e., physician, counselor, therapists [speech,
behavior, physical, occupational], psychologist, etc.) throughout their life, utilizing
pharmacological (i.e., Selective Serotonin Reuptake Inhibitors and Antipsychotics) and nonpharmacological treatments (i.e., Applied Behavioral Analysis [Dunlap, Kern-Dunlap, Clarke, &
Robbins, 1991; Virués-Ortega, 2010], Pivotal Response Treatment [Koegel & Kern Koegel,
2006; Lei & Ventola, 2017], Verbal Behavior [Sundberg & Michael, 2001], Early Start Denver
Model [Smith, Rogers, & Dawson, 2008], and Relationship Development Intervention [Gutstein
& Sheely, 2002]). While each of these methods addresses specific symptomologies associated
with the disorder, a growing number of research studies have suggested that these
symptomologies may be related to deficits in cognitive control (Hill, 2004; Hughes et al., 1994;
Ozonoff & Jensen, 1999) consequently providing an alternative avenue to address impairments
associated with ASD.
Cognitive Control
Cognitive control, also referred to as executive function or executive control, is a term
used to reference the underlying set of higher-order, cognitive processes associated with the
regulation of goal-directed interactions within the environment (Botvinick et al., 2001; Meyer &
Kieras, 1997; Voss et al., 2011). Comprised of three core cognitive processes (i.e., inhibition,
working memory, and cognitive flexibility; Diamond, 2006)), cognitive control is involved with
scheduling, selection, maintenance, and coordination of processes underlying an individual’s
perception, memory, and behaviors (Botvinick et al., 2001; Hillman et al., 2008; Miyake et al.,
2000; Pontifex et al., 2011). Although each of these core processes plays an integral role in an
individual’s decision making, and perceptions of the world, inhibition has received a great deal
of attention within the literature, particularly with respect to research involving acute physical

9

activity (Hillman et al., 2006; Hillman, Snook, & Jerome, 2003; Hillman, Buck, Themanson,
Pontifex, & Castelli, 2009; Pontifex & Hillman, 2007). Cognitive psychologists have theorized
that inhibition is composed of related, but distinct subprocesses (Nigg, 2000) each playing a part
in an individual’s ability to override impulsive responses. To date, researchers have been able to
distinguish subtypes of inhibition allowing for a more thorough examination of how this
overarching process operates (Friedman & Miyake, 2004; Nigg, 2000). Inhibition is commonly
separated into prepotent response inhibition (i.e., the ability to suppress a dominate response in
order to respond with a less potent response) and interference control (i.e., the ability to ignore
irrelevant information within the stimulus environment; Barkley, 1997; Friedman & Miyake,
2004; Geurts, van den Bergh, & Ruzzano, 2014; Miyake & Friedman, 2012), with various
paradigms used to examine each.
Of the paradigms utilized to assess inhibition processes of cognitive control, the Eriksen
flanker task (Eriksen & Eriksen, 1974) is commonly used due to its ability to elicit interference
control. In order to successfully complete this task, participants are required to respond to a
centrally presented target stimulus flanked by task irrelevant stimuli with the congruence of the
target stimulus and flanking stimuli manipulated to elicit response interference. During a
congruent stimuli presentation (i.e., <<<<< or PPPPP), the centrally presented target stimulus
and the lateral flanking stimuli are uniform. As this presentation array does not require
interference control, task performance commonly results in faster and more accurate responses
when compared to the incongruent stimuli presentation (i.e., <<><< or PPRPP). The
incongruent array utilizes opposing action-schemas for the target and flanking stimuli, eliciting
both an incorrect response to the flanking stimuli and a correct response to the target stimuli
(Eriksen & Eriksen, 1974; Pontifex et al., 2015). Due to the additional interference presented

10

during this stimulus condition, participants are required to gate out the irrelevant task
information in order to engage in a correct response (Spencer & Coles, 1999). While
conceptually this paradigm appears simplistic, it is because of this straightforward approach that
this task has been adapted and modified to fit many diverse needs. Through the modification of
the stimuli presented (e.g., letters, arrows, fish; Eriksen & Eriksen, 1974; Hillman et al., 2003;
Pontifex et al., 2013), researchers have been able to adapt the task to meet their needs for various
participant populations and examine a variety of outcome measures. However, it is important to
note that when examining these varying populations, selection of outcome measures to explore is
an important consideration. While reaction time can be used to explore the timing for response
selection within most populations, children have been shown to exhibit impulsiveness when
selecting a response that may result in consistency of reaction time across each condition
(congruent and incongruent; Christakou et al., 2009; Davidson et al., 2006; Drollette et al.,
2014). Therefore, response accuracy measures may provide more accurate, insight into the
ability to inhibit the flanking stimuli given the population of interest (Davidson et al., 2006;
Drollette et al., 2014).
Cognitive Control Characteristics in Individuals with ASD
As we have acquired more information pertinent to our understanding of the core
cognitive control aspects, application of this knowledge to new populations has increased
exponentially. In particular, interest toward individuals diagnosed with developmental
disabilities, such as ASD, has grown due to the similarities between the symptomologies
associated with diagnoses and the core cognitive processes. Through this work, one proposed
explanation for a potential underlying mechanism associated with ASD symptomologies has
been based on impairment in the aspects of cognitive control (Hill, 2004; Hughes et al., 1994;

11

Ozonoff & Jensen, 1999). Within this population, deficits associated with cognitive flexibility
(see Hill, 2004 for review), working memory (Luna et al., 2002; Luna, Doll, Hegedus, Minshew,
& Sweeney, 2007) and inhibition (Christ et al., 2007; Geurts, Verte, Oosterlaan, Roeyers, &
Sergeant, 2004; Ozonoff, Strayer, McMahon, & Filloux, 1994) have all been documented.
Findings relative to cognitive flexibility and working memory impairment have been supported
through extensive assessment of structural, metabolic, and neurotransmitter abnormalities
associated with the prefrontal cortex (PFC; Chugani et al., 1997; Ohnishi et al., 2000; Salmond,
De Haan, Friston, Gadian, & Vargha-Khadem, 2003), a region of the brain associated with
higher-order cognitive processing, such as cognitive control, and has shown delayed
development in individuals diagnosed with ASD (Zilbovicius et al., 1995). As evidence to
substantiate the findings for deficits in cognitive control and working memory remains
consistent, research exploring inhibition within the population has been debated.
Although the aspect of inhibition has yielded mixed findings with regard to individuals
with ASD, it has also been consistently suggested as the link for ASD symptomologies due to the
impaired ability of individuals with ASD to suppress unwanted behaviors (Langen, Durston,
Kas, van Engeland, & Staal, 2011). Additionally, individuals with ASD struggle to inhibit
extraneous semantic information during conversation often strictly interpreting language,
conveying a potential influence of inhibitory control on social and communicative interaction
(Geurts et al., 2014; Hughes, 2001). While the notion that inhibition may underlie ASD
symptomologies is prevalent, the inconsistencies within the literature have complicated our
understanding of inhibitory control within an ASD population. However, it is because of these
inconsistencies that researchers have begun separating inhibition into prepotent response
inhibition and interference control when investigating this research question. An example of this

12

can be seen in Christ et al.’s, 2007 and 2011 work utilizing a flanker paradigm to assess
interference control, and a Go/No-Go task to assess inhibition of prepotent responses. Findings
from this study indicated that individuals with ASD experience impaired interference control, but
have intact prepotent response inhibition when compared to a control population. While there
are studies suggesting that interference control remains intact for individuals diagnosed with
ASD (Brian, Tipper, Weaver, & Bryson, 2003; Ozonoff & Strayer, 1997, p. 200; Ozonoff et al.,
1994; Pennington & Ozonoff, 1996), research has shown that when utilizing tasks that require
the individual to manage task irrelevant information (such as the Flanker paradigm) the deficits
observed for inhibition appear specific to the interference control domain (Adams & Jarrold,
2012; Keehn et al., 2010; Lopez et al., 2005). Interestingly, the deficits observed in interference
control for individuals with ASD mirror the aspects of cognitive control that have shown
transient enhancements following an acute bout of physical activity (Drollette et al., 2014;
Hillman, Buck, et al., 2009; Pontifex et al., 2013), suggesting a potential for physical activity to
improve interference control performance and reduce symptomologies in individuals with ASD.
Physical Activity Trends in Individuals with ASD
While research exploring the effects of physical activity on aspects of cognitive control
are still fairly contemporary, the benefits of physical activity associated with overall health have
been well documented. The Centers for Disease Control and Prevention (U.S. Department of
Health and Human Services, 2008) have reported that participating in 150 minutes per week of
physical activity at a moderate-to-vigorous intensity level can help to reduce risk of
cardiovascular disease, type II diabetes, obesity, some forms of cancer, and improve mental
health. Current guidelines also recommend that youth between the ages of 6 and 17 years old
should engage in at least 60 minutes of physical activity daily, including aerobic, resistance, and

13

bone-strengthening activities (U.S. Department of Health and Human Services, 2008). Despite
this knowledge, estimates indicate that 1 in 5 adults and approximately 21% of youth in the U.S.
meet these recommendations (U.S. Department of Health and Human Services, 2008). In
response to this trend, there has been an increased emphasis placed on motivation and adherence
to physical activity by researchers in many fields. However, while public health officials have
attempted to implement the findings from this research, it has become evident that activity
patterns associated with various populations, such as individuals diagnosed with developmental
disabilities, differ from the general public. Preliminary research has shown these individuals may
exhibit an increased sedentary lifestyle when compared to their typically developing peers
(Draheim, Williams, & McCubbin, 2002; Todd & Reid, 2006), with individuals diagnosed with
ASD exhibiting a significant decline in physical activity participation with age (MacDonald,
Esposito, & Ulrich, 2011; Pan, 2008; Pan & Frey, 2006). This increased sedentary behavior not
only places these individuals in a high-risk category for the aforementioned health concerns, but
also suggests an even greater need to explore motivation for activity in these groups, as well as,
garner a better understanding for the perceived barriers to physical activity these individuals are
facing.
One barrier to physical activity often cited throughout the ASD literature is related to
impairments in the areas of fine and gross motor skills, commonly presenting as delayed or
atypical motor patterns (Lloyd, MacDonald, & Lord, 2013; Ozonoff et al., 2008; Staples & Reid,
2010). Over the course of an individual’s life there are a number of milestones associated with
skill development that each person will experience. Progression through these milestones is a
fairly set continuum with most deviations from the standard trajectory occurring through changes
in the onset and endpoints with each milestone. Individuals diagnosed with ASD tend to fall

14

behind the standard trajectory early in life, and with age may experience an exponential increase
in delays due to the compounding nature of the milestone progression (Lloyd et al., 2013).
When considering the critical role these motor milestones play in the acquisition and utilization
of complex motor skills associated with physical activity (i.e., kicking a soccer ball, throwing,
catching, etc.) it is understandable that this population struggles to maintain an active lifestyle
when compared to their typically developing peers. Exacerbating this issue further, exposure to
these activities and practice at home, a playground, or during physical education classes is often
used to address these delays, however these settings tend to introduce additional barriers for
children with ASD potentially effecting the ability for these individuals to overcome motor
deficits.
Beyond improving physical activity engagement and motor delays, research focusing on
the effects of physical activity within this population have also had promising results related to
some of the symptomologies commonly associated with ASD. When individuals diagnosed with
ASD are observed in the playground and physical education environments, concerns affiliated
with deficits observed in social and communicative interaction are often cited in the literature. In
an unstructured setting utilizing free play activity, such as recess, research has shown that
individuals diagnosed with ASD tend to exhibit lower levels of engagement in physical activity
compared to their TD peers (Pan, 2008). Similar findings have also been observed in the
playground environment, where the ASD population gravitate toward individualized activities
such as isolated play (playing by oneself), parallel play (playing near a peer, but not interacting),
and observation (watching other’s play while they remain inactive). With regard to physical
activity, this form of isolation can be beneficial as it helps to eliminate issues associated with
non-verbal communication and feeling misunderstood while attempting to complete the activity

15

(Pan, 2009). Yet, as most physical activities individuals participate in at recess and on the
playground, involve group participation, children with ASD who disengage from their peers may
be isolated from these activities due to their difficulties with social interaction (Pan & Frey,
2006), and find themselves physically inactive as a result. Contrary to this dynamic, physical
education classes appear to provide a greater level of opportunity for social engagement allowing
individuals with ASD to become more active (Pan, 2008). It has been suggested that the change
in environment, particularly the transition to a structured setting, could be directly related to the
change in participation level. As the different environments present with varying levels of peer
acceptance, fear of exclusion, social engagement, and other negative social and behavioral
perceptions, individuals with ASD may not perceive an equal opportunity for involvement
between the settings (Pan, 2008, 2009; Pan, Tsai, Chu, & Hsieh, 2011). For instance, in the
instructor-led environment of a physical education classroom, a pre-determined social and
communicative framework (i.e., kids are automatically involved and part of the team) may
optimize the opportunity for individuals with ASD to interact with peers and engage in greater
amounts of physical activity. Consistent with these findings, Pan (2010) conceptually expanded
this approach from the physical education environment to a group exercise setting (aquatics)
over the course of 10-weeks. Results from this study indicate improved social skills following
completion of the exercise protocol among 16 children with ASD. However, in a similar study
conducted by Fragala-Pinkham, Haley, & O'Neil (2011) over the course of 14-weeks, no
differences were observed between the control group (n = 5) and those diagnosed with ASD (n =
7). Although results have been mixed, further research exploring potential benefits of physical
activity on social and communicative interaction within this population is necessary as concerns
related to statistical power within the samples and potential confounds (i.e., repeated

16

socialization opportunities through chronic intervention protocols) may have influenced the
findings present in existing literature.
Research exploring the effects of physical activity on repetitive and restrictive behaviors
has been encouraging, with many of the studies focusing on improvements in classroom etiquette
and unwanted behaviors. This area of study has shown that when individuals with ASD engage
in physical activity, stereotypic behaviors often associated with repetitive and restrictive
behaviors seen in the classroom improve (Elliott, Dobbin, Rose, & Soper, 1994; Kern, Koegel, &
Dunlap, 1984; Kern, Koegel, Dyer, Blew, & Fenton, 1982). Of particular concern within this
literature, though, is the variation in physical activity modalities and the varying study designs.
Within the small number of studies that have explored this effect, majority have utilized an
aerobic bout of physical activity within the study design (i.e., jogging; Kern et al., 1984, 1982;
Levinson & Reid, 1993). This approach is consistent with physical activity based research,
however as each of these studies utilizes an observational analytic cohort design, findings from
these analyses are informative but require further exploration. Alternatively, the few studies
implementing an experimental crossover design are well powered and controlled, however the
physical activity modalities include martial arts (Bahrami, Movahedi, Marandi, & Abedi, 2012;
Movahedi, Bahrami, Marandi, & Abedi, 2013) and horseback riding (Bass, Duchowny, &
Llabre, 2009; Gabriels et al., 2012) potentially complicating the generalizability of the data to the
academic setting. In spite of these concerns, all but one study (Oriel, George, Peckus, & Semon,
2011) exploring this area has observed a positive effect of physical activity on stereotypic
behaviors, suggesting that increased physical activity participation may lead to potential
reductions in unwanted behaviors in daily life as well as within the classroom. Unfortunately,
regardless of the benefits associated with physical activity, individuals with ASD who have

17

overcome many of the barriers limiting their participation are still often unable to engage in
physical activity as current educational practices have reduced or eliminated physical activity
options in favor of increased instructional time within the classroom (Pan, 2008).
Acute Physical Activity Influences on Physical and Cognitive Health
Concomitant with recent societal trends indicating an increase in sedentary behavior, and
the rise in obesity for both children and adults within the U.S. (Finkelstein et al., 2012; Ogden,
Carroll, Kit, & Flegal, 2014), interest in finding ways to combat these adverse health
behaviors/characteristics has grown. Experts have suggested for the first time in 200 years, that
younger generations may face shorter life expectancies relative to their parents and grandparents
(Olshansky et al., 2005). Given the well-established links between physical activity and many
serious health conditions (i.e., cardiovascular disease, obesity, some forms of cancer, diabetes,
metabolic syndrome; U.S. Department of Health and Human Services, 2008; Yanovski &
Yanovski, 2011) that may play a role in this diminished life expectancy, researchers have sought
ways to promote and encourage a physically active lifestyle. Particular interest has been given to
the school environment for children and young adults, as the restrictions of the environment (i.e.,
confined to desk, limited activity time, approximately 30% of a day [7-8 hours] spent in this
setting) may inadvertently foster sedentary behaviors. Therefore, advocates for increased
physical activity in children, adolescents, and young adults have focused their efforts on the
school environment, suggesting policy changes that would provide greater opportunity to
become active throughout the school day (H. Wechsler, Devereaux, Davis, & Collins, 2000).
These changes in policy, however, have been met with resistance, with school
administrators/educational policy makers often citing concerns about allocating time of

18

objectives outside of the classroom that may impact a child’s academic performance (Dwyer,
Sallis, Blizzard, Lazarus, & Dean, 2001; Shephard, 1997).
While these concerns are understandable, given the increased pressures placed on school
officials and teachers through initiatives like the 2001 No Child Left Behind Act (NCLB), one
can conclude that these concerns for a negative impact on academic performance appear
unfounded based on current literature in the field (Castelli, Hillman, Buck, & Erwin, 2007;
Chomitz et al., 2009; Coe, Pivarnik, Womack, Reeves, & Malina, 2006; Dills, Morgan, &
Rotthoff, 2011; Dollman, Boshoff, & Dodd, 2006; Eveland-Sayers, Farley, Fuller, Morgan, &
Caputo, 2009; Fisher et al., 2011; Grissom, 2005; Keita Kamijo et al., 2012; Li, Dai, Jackson, &
Zhang, 2008; Pontifex et al., 2011). The NCLB Act has focused on increasing accountability
standards for teachers and school’s relative to reading and mathematics performance within the
classroom, resulting in 44% of schools reporting decreases in non-academic subjects in order to
increase classroom time dedicated to math and reading (McMurrer & Kober, 2007). This
method of selectively dedicating time to specific academic material at the expense of others may
be counterintuitive, however, as programs implementing social, emotional and physical
development appear to be the most effective (Diamond, 2010). In particular, researchers have
shown that when including recess within the school day, classroom behavior and on-task
performance significantly improve (Jarrett et al., 1998), and this added activity during the day
can also translate to increased physical activity outside of the school day (Dale, Corbin, & Dale,
2000). Unfortunately, despite the growing body of evidence supporting the health and classroom
benefits of physical activity, the decline in physical activity opportunities throughout the school
setting continues. However, with recent evidence suggesting that engagement in a single-bout of
physical activity may serve to benefit cognitive function (particularly cognitive control; Drollette

19

et al., 2014, 2012; Hillman, Pontifex, et al., 2009; Pontifex et al., 2013), researchers have
potentially forged a new link between the cognitive learning process and physical activity
suggesting that a short-bout of activity (such as recess or PE) may not only improve overall
health and classroom behavior, but it may improve the way in which our brain functions to learn
material within the classroom.
To date the literature exploring the effects of physical activity on cognitive health has
primarily focused on a chronic approach due to the physical activity guidelines given through
public health recommendations (U.S. Department of Health and Human Services, 2008). These
guidelines, while beneficial and informative, often emphasize the habituation of physical activity
into a chronic lifelong behavior, which for many is not maintainable and may discourage
physical activity engagement (Okun et al., 2003). Therefore, recent studies have deviated from
the utilization of intervention strategies to assess chronic cognitive benefits, to focus on singlebouts of physical activity (i.e. acute physical activity), with many researchers suggesting that the
chronic benefits observed in the literature may represent a culmination of improvements incurred
through several individual physical activity bouts. It is important to note, however, that the
mode and intensity of these acute bouts is crucial for our understanding of physical activity
benefits on cognition. Currently, aerobic-based physical activity has been the primary mode of
physical activity utilized within the literature (Hillman, Pontifex, et al., 2009; Pontifex et al.,
2015; Pontifex et al., 2013), with few studies exploring the effects of resistance training (Chang,
Pan, Chen, Tsai, & Huang, 2012; Kao, Westfall, Parks, Pontifex, & Hillman, 2017; Lachman,
Neupert, Bertrand, & Jette, 2006) and coordination exercises. The reasoning for this approach
has relied on the understanding that continuous treadmill- or cycle-based activities allow the
researcher to control the intensity of the exercise and maintain a steady-state of activity.

20

However, as researchers have expanded practices to other modalities, similar benefits have been
reported in areas such as resistance training (Kao et al., 2017). These findings, while promising,
must also be approach cautiously as the authors utilized a circuit training approach to the
resistance training condition, potentially altering the basis of the activity from anaerobic to
aerobic in nature. However, we cannot conclude that simply participating in aerobic based
activity will lead to benefits in cognitive function, as research has indicated that intensity level
may moderate the effects of physical activity on cognition, with moderate-to-vigorous activity
levels yielding the greatest change compared to light and vigorous intensities (Chang et al., 2012;
Hillman, Kamijo, & Pontifex, 2012; Lambourne & Tomporowski, 2010).
Presently, the most commonly explored component of cognitive control relative to
physical activity has been the aspect of inhibition (Hillman, Buck, et al., 2009; Hillman et al.,
2003; Kamijo et al., 2009; Pontifex et al., 2015, 2013). In an early study, Hillman and
colleagues (2009) implemented a 20-minute bout of moderate intensity treadmill walking to
assess changes in performance on a modified flanker task relative to a seated rest condition.
Findings from this study indicated that following physical activity children exhibited greater
response accuracy when compared to seated rest, suggesting that participation in a single-bout of
moderate aerobic physical activity may improve a child’s ability to effectively gate out irrelevant
information. Interestingly, in a separate study by Drollette et al. (2014), similar findings relative
to response accuracy were found indicating that aerobic physical activity may have a particular
influence of this outcome measure. However, within this study, participants were separated
based on flanker performance during their seated rest condition into high and low performing
groups, with the effect of physical activity on response accuracy only manifesting within the low
performing group. These findings suggest that the benefits associated with inhibitory control

21

may differentially impact individuals based on who needs it the most. This assertion has
provided retroactive support for studies exploring these effects in populations diagnosed with
developmental disabilities, such as ADHD. Due to the similarities between ADHD
symptomologies and inhibitory control aspects influenced via physical activity, Pontifex and
colleagues (2013) sought to investigate if similar effects on inhibition may be observed within a
preadolescent population diagnosed with ADHD. Comparing between a 20-minute aerobic
physical activity condition and a 20-minute seated reading condition, researchers identified that
following the acute physical activity condition individuals in their typically developing group
and those with ADHD shown improved response accuracy. However, while research in this area
has been promising, in order to better understand the influence of physical activity on inhibition,
further investigations within other participant populations is needed. Accordingly, with the
known deficits in inhibitory control associated with ASD, this population may be uniquely
qualified to experience these benefits
Purpose
Evidence suggests that children with ASD exhibit cognitive deficits related to
interference control, an aspect of cognition that is positively influenced by a single bout of
aerobic physical activity. Therefore, this study explores the influence of an acute bout of
moderate intensity aerobic activity on cognition in children with ASD. This study will focus on
how single bouts of physical activity may result in variations in behavioral indices of
interference control. Utilizing a well-controlled design including age-matched typically
developing control participants and a reading control condition, results from this study will
elucidate the influence of physical activity on interference control in children, and to what extent

22

this relationship may translate to the deficits in interference control observed in children with
ASD.
Rationale
A number of research studies have continued to highlight the growing struggle faced by
children with ASD. With many of these individuals participating in a variety of treatment
techniques (i.e., pharmacological, behavioral, social, and educational), the cost and time
constraints can be overwhelming. As physical activity may provide a cost-effective and
relatively low time demand, particularly in a population that has a high prevalence of sedentary
behaviors, the inclusion of activity may help address these concerns. Therefore, the proposed
study explores the relationship between cognitive control processes, specifically the interference
control component of inhibition, and a single bout of aerobic physical activity in both typically
developing preadolescent children and children with ASD. Evidence of a meaningful relationship
between activity and interference control in children with ASD, may provide insight into an
alternative method for improving classroom performance and cognitive health, as well as a
complementary non-pharmaceutical option aiding children diagnosed with ASD.
Hypotheses
The purpose of this investigation was to determine the effects of a single bout of physical
activity on behavioral indices of cognition for children with ASD during performance of a task
requiring variable amounts of inhibition, specifically interference control. Accordingly, given
prior research in this area the following specific hypothesis are proposed:
1. Prior to the experimental conditions, it was predicted that children with ASD would
manifest with poorer response accuracy relative to typically developing children;
indicating impairments in interference aspects of inhibitory control.

23

2. Relative to seated reading, it was predicted that a single bout of physical activity would
enhance response accuracy for both children with ASD and typically developing children,
indicating that aerobic physical activity is beneficial to behavioral indices of interference
control.
3. Relative to typically developing children, participation in a single bout of physical
activity would result in selectively greater improvements in response accuracy for
children with ASD, such that differences between groups would no longer be apparent
following physical activity.

24

CHAPTER 3
Methodology
The relationship between a single bout of aerobic physical activity and modulations in
cognitive control in children with ASD was investigated. A sample of individuals with ASD and
typically developing peers were recruited from the Greater Mid-Michigan area. Each participant
completed an assessment of inhibitory aspects of cognitive control prior to and following 20
minutes of aerobic physical activity and 20 minutes of seated reading.
Participants and Recruitment
A sample of 18 individuals diagnosed with ASD (0 female) and 18 typically developing
individuals (4 female) were recruited to participate. The participant sample was predominately
male, as the ratio of ASD diagnoses for males and females is 5:1 (Baio, 2014). The average age
for participants in the ASD group was 12.7 ± 1.0 years old, while the TD group had an average
age of 12.3 ± 1.1 years. Children with ASD were recruited from the general community
population based upon a clinical diagnosis along the autism spectrum. Recruitment took place
through: 1) fliers posted throughout the greater Mid-Michigan area (see Appendices H & J), 2) email correspondence with parents and families of potential participants (see Appendices I & K),
3) support from health care professionals who specialize in ASD treatment, and 4) support from
regional organizations who work to connect parents, inform the community, and help children
diagnosed with ASD. Clinical status was verified using the Autism Diagnostic Observation
Schedule-2nd Edition (ADOS-2). Participants over the age of 18 provided written consent prior
to beginning the study, and participants under the age of 18 provided written assent along with
written consent from their parent or legal guardian in accordance with the Human Research
Protection Program at Michigan State University.

25

Exclusionary criteria.
Inclusionary criteria for all participants, as well as specific inclusionary criteria for ASD
and typically developing participants are provided in Table 3.1. Non-consent of the individual or
a child’s guardian resulted in the participant being excluded from the investigation. Any
participant who was not capable of performing exercise based on the Physical Activity Readiness
Questionnaire (PAR-Q; Thomas, Reading, & Shephard, 1992) was excluded for their safety.
Similarly, all participants had normal or corrected-to-normal vision. Any potential participant
with an ASD diagnosis who was non-verbal was also excluded from the study. Intelligence
quotient, assessed using the Wechsler Abbreviated Scale of Intelligence – Version 2 (WASI-II;
Wechsler, 2011), and pubertal timing, measured using the modified Tanner Staging Scales
(Taylor et al., 2001), were also obtained as cognition has been found to be sensitive to these
factors (Davies, Segalowitz, & Gavin, 2004). While not used for exclusionary criteria, these
factors were assessed as potentially confounding variables within the statistical model.
Power Analysis
A sensitivity analysis was conducted using G*Power 3.1.2 (Faul, Erdfelder, Lang, &
Buchner, 2007) to determine the relative effects that would be possible to observe given the
present experimental design. To provide a conservative assessment, a participant attrition rate of
approximately 30% was assumed across multiple days of testing. Thus, given a sample size of 18
participants per group and beta of .20 (i.e., 80% power), the present design theoretically has
sufficient sensitivity to detect multivariate repeated measures within-factors effects exceeding f =
0.207, between-factors effects exceeding f = 0.38 (assuming correlation between repeated
measures ≥ 0.5), and interactions exceeding f = 0.585. For post-hoc comparisons, assuming a
two-sided alpha, the design has sufficient sensitivity to detect t-test differences exceeding d =

26

0.48 for dependent means and d = 0.96 for independent means. Within the context of the acute
physical activity and cognition literature, a previous investigation conducted by Pontifex et al.
(2013) observed acute physical activity induced modulations in inhibitory control in typically
developing children and children with ADHD with an effect size in excess of d = 0.9. Similarly,
effect sizes for ASD related impairments in inhibitory control have been observed to exceed d =
1.0 (Christ et al., 2007). Thus, even with such an attrition rate, this design should provide
sufficient sensitivity to address the aims of the present investigation.
Table 3.1.
Inclusion Criteria for Participant Acceptance into the Current Project
Inclusion Criteria for All Participants
1. 5–25 years of age.
2. Physically capable of performing exercise based on the PAR-Q.
3. Normal or corrected-to-normal vision.
Inclusion Criteria for ASD participants

Inclusion Criteria TD participants

1. Verified clinical status using the ADOS.

1. Free of ASD diagnosis

2. Verbal

2. SNAP-IV ADHD-Inattention subscale
average score below 1.78
3. SNAP-IV ADHD-Hyperactivity/Impulsivity
subscale average score below 1.44
4. SNAP-IV ADHD-Combined subscale
average score below 1.67

Note: Physical Activity Readiness Questionnaire – PAR-Q

Cognitive Control Task
To assess inhibitory aspects of cognitive control, participants completed a modified
version of the Eriksen flanker task (Eriksen & Eriksen, 1974). This task requires participants to
attend to a centrally presented target fish amid either congruous or incongruous flanking fish (see

27

Figure 3.1), with the goal of responding based on the directionality of the target stimuli. The
incongruent stimuli (when the target faces opposite the direction of the flanking stimuli), relative
to the congruent stimuli (when all stimuli face the same direction) requires greater amounts of
interference control to inhibit the activation of the incorrect action schemas elicited by the
flanking stimuli, in order to over-ride this response pattern to execute correct response (Spencer
& Coles, 1999). Participants completed two blocks of 156 trials, presented with equiprobable
congruency and directionality. The block of trials was restarted if participants exhibited
performance below 50% correct or exhibited a high rate of impulsive responses (TD: RestPretest = 2, Rest-Posttest = 0, Exercise-Pretest = 2, Exercise-Posttest = 3; ASD: Rest-Pretest = 2,
Rest-Posttest = 2, Exercise-Pretest = 5, Exercise-Posttest = 3). The stimuli were 3 cm tall yellow
goldfish, presented focally for 200 ms on a blue background with an inter-trial interval equally
distributed between 1500 ms, 1600 ms, and 1700 ms. Utilization of this task allowed for the
assessment of a number of behavioral performance indices. Primary analysis utilized reaction
time (RT; i.e., time in ms from the presentation of the stimulus) and response accuracy (i.e.,
number of correct and error responses) measures in addition to interference score measures
(incongruent minus congruent trials). Stimulus presentation, timing, and measurement of
behavioral response time and accuracy was controlled using PsychoPy, 1.81 (Peirce, 2009).
Experimental Conditions
Participants complete two experimental conditions in the study, a 20-minute walk on a
treadmill and 20-minutes of seated reading. In the exercise condition, the first 4-minutes of the
condition were utilized as a warm up for the activity. During this warm-up, participants would
begin by walking at a slow pace at a 1.0% grade with the speed and grade of the treadmill
increased incrementally until heart rate reached 65% of the participants age-predicted heart rate

28

max (i.e., 220-age). Upon reaching the 65% threshold, the speed and grade of the treadmill
remained unchanged until the end of the condition. During the session, participants were able to
interact with the research staff, however no other activities were provided during this time. The
rest condition similarly lasted for 20-minutes with participants given the option of reading either
a book of their choice from a selection of age-appropriate books, or bringing a book from home
to read. During this time, participants were given a desk to sit at where they were able to
complete the reading task on their own. Research staff sat with the children during this time, and
had minimal interaction. Throughout each condition, heart rate, rate of perceived exertion
(assessed with the OMNI scale), and feeling scale were assessed every 2-minutes; with speed
and grade of the treadmill also measured at the same intervals during the exercise condition.

Figure 3.1. Illustration of the congruent (A) and incongruent (B) goldfish stimuli used in the
modified flanker task.
Procedure
A within-subjects repeated measures design was utilized for this study, during which
participants were asked to visit the lab for three separate sessions occurring on three different
days. Session 1 was approximately 2-hours in duration, while sessions 2 and 3 were
approximately 1.5 hours in duration. On the first day, participants and parents completed all

29

paperwork, including; informed consent, informed assent, health history demographics (HHD),
physical activity readiness questionnaire (PAR-Q), the SNAP-IV Rating Scale for AttentionDeficit/Hyperactivity Disorder (ADHD), the Social Communication Questionnaire (SCQ), the
modified Tanner Staging Scales for current pubertal staging, and the Wechsler Abbreviated
Scale of Intelligence – Version 2. Initial participant recruitment included 22 participants with
ASD, and 21 TD participants. At this stage, 2 potential participants with ASD were dropped
from the study as one was non-verbal, and the other had photosensitive epilepsy that may have
been impacted by the cognitive task. All recruited members of the TD group completed the 1st
session.
Following completion of the paperwork, participants in the ASD group — based on prior
diagnosis — were assessed using the Autism Diagnostic Observation Schedule – 2nd edition
(ADOS-2) as an added measure of classification and severity of their diagnosis. All ADOS-2
assessments were conducted by trained, clinically reliable researchers. Upon completion of the
questionnaires and the ADOS-2 assessment (only for ASD group), participants completed a
practice set of each of the experimental procedures that will be used for the study. This practice
included a brief exposure to the modified flanker task, including one practice block consisting of
20 trials and one full block of 156 trials.
Participants were then counter-balanced into two different session orders, with some
participants receiving the reading session on the second day and the aerobic physical activity
session on the third day. The alternative order had participants receiving the aerobic physical
activity session on the second day and the reading session on the third day. Consistency between
time of day for each session was attempted with an average difference in session start time of 0.1
hours (± 2.4 hours). During each visit, heart rate (HR) was measured at 2-minute intervals

30

throughout the entire session using a Polar heart rate monitor (Polar WearLink®+ 31, Polar
Electro, Finland). Prior to the start of the testing on each day, participants were provided with a
block of 20 practice trials of the flanker task. Participants were then asked to complete the
flanker task prior to and 10 minutes following each experimental session. The experimental
conditions consisted of 20 minutes of either seated reading or aerobic physical activity on a
motor-driven treadmill at an intensity between 65% and 75% of their age predicted maximum
heart rate (Pontifex et al., 2013). The final sample included 18 participants in each group, with an
81.8% and 85.7% attrition rate for the ASD and TD groups, respectively. Prior to beginning the
2nd session, one member of the ASD group withdrew from the study, and two members of the
TD group withdrew from participation. Prior to the 3rd session, one member of each group
withdrew from participation. Upon completion of the study, participants were compensated
monetarily at a rate of $10 per hour for all time completed in the study.
Statistical Analysis
Statistical analysis was conducted using PASW Statistics, 24.0 (IBM, Armonk, NY)
using the Greenhouse-Geisser statistic with subsidiary univariate ANOVAs and Bonferroni
corrected t-tests for post-hoc comparisons. The family-wise alpha level was set at 0.05, with
effect sizes reported using partial-eta squared and Cohen’s d based on the appropriate corrections
for between-subjects (ds) and repeated measures (drm). Prior to hypothesis testing, preliminary
analyses were conducted to ensure that the ASD and typically developing groups did not
significantly differ on any factors known to influence cognitive function in this age group (e.g.,
SES, age, pubertal timing, IQ, etc.). Analysis of task performance measures (median RT and
response accuracy) was conducted separately using a 2 (Group: ASD, TD) × 2 (Mode: Reading,
Exercise) × 2 (Time: Pre-test, Post-test) × 2 (Congruency: Congruent, Incongruent) multivariate

31

repeated measures ANOVA. Secondary analyses examined task performance interference scores
(Incongruent minus Congruent trials) using a 2 (Group: ASD, TD) × 2 (Mode: Reading,
Exercise) × 2 (Time: Pre-test, Post-test) multivariate repeated measures ANOVA. To ensure that
any potential findings were not masked by differences in pre-test performance, analyses were
also conducted replicating the models listed above but collapsing Time into a change score (Posttest minus Pre-test; Pontifex et al., 2015).

32

CHAPTER 4
Results
Participant Characteristics
Participant demographics and clinical status confirmation statistics for the ASD group are
provided in Table 6.1 (Appendix P). Initial analyses of demographic variables between groups
indicated a significant difference for IQ, t (34) = 2.6, p = 0.014, ds = 0.87, 95% CId [0.18, 1.55].
As a result, analyses were conducted including IQ as a covariate to examine if IQ related to any
task performance variables within the multivariate repeated measures ANOVA models. Findings
revealed no significant interactions with mode (p’s ≥ 0.063); therefore, all further analyses were
collapsed for IQ. No other significant differences were identified for age, pubertal timing, or
socioeconomic status (SES), t’s (34) ≤ 1.8, p’s ≥ 0.075, ds’s ≤ 0.61, 95% CId [-0.06, 1.28].
Clinical status confirmation for the ASD group, separated by ADOS module, are available in
Table 6.2 (Appendix P). Findings also revealed no significant differences between groups for
HR across either condition, t’s (33) ≤ 1.7, p’s ≥ 0.097, ds’s ≤ 0.58, 95% CId [-0.10, 1.25] (Figure
6.13; Appendix Q). Finally, preliminary analysis revealed no significant difference between
groups for session order, 2 (1, N = 36) = 2.857, p = 0.091, therefore all subsequent analyses were
collapsed across session order.
Task Performance
Reaction time.
Analysis revealed a main effect of Congruency, with incongruent trials (486.4 ± 21.6 ms)
exhibiting longer RT latency when compared to congruent trials (458.6 ± 21.2 ms), F (1, 34) =
59.1, p < 0.001, ƞp2 = 0.64. A Group x Mode x Time interaction was also observed for RT
latency, F (1, 34) = 4.7, p = 0.038, ƞp2 = 0.12. Decomposition of this interaction revealed faster

33

reaction time at rest (465.2 ± 145.3 ms) relative to exercise (497.3 ± 148.7 ms) only at pretest for
the ASD group, t (17) = 2.6, p = 0.017, drm = 0.22, 95% CId [0.04, 0.39] (Figure 6.14; Appendix
Q). No significant differences were observed at posttest or from pre- to posttest for either group,
F’s (1, 34) ≤ 2.8, p’s ≥ 0.1, ƞp2’s ≤ 0.14.
Response accuracy.
Analysis of response accuracy revealed a main effect of Group, with poorer overall
response accuracy for the ASD group (70.6 ± 3.7 %) relative to their TD counterparts (82.0 ± 3.7
%), F (1, 34) = 4.8, p = 0.035, ƞp2 = 0.13. Additionally, a main effect of Congruency, F (1, 34) =
24.2, p < 0.001, ƞp2 = 0.42, was observed with lower response accuracy for the incongruent trials
(70.2 ± 3.1 %) relative to congruent trials (82.5 ± 2.6 %) (Figure 6.15; Appendix Q).
Interference scores.
Analysis for interference scores associated with the flanker task (incongruent trials minus
congruent trials) revealed no significant findings for either mean RT latency (Figure 6.16a;
Appendix Q), F’s (1, 34) ≤ 1.1, p’s ≥ 0.3, ƞp2’s ≤ 0.03, or response accuracy (Figure 6.16b;
Appendix Q), F’s (1, 34) ≤ 2.2, p’s ≥ 0.1, ƞp2’s ≤ 0.06.
Change scores.
Analysis of change in performance from Pretest to Posttest revealed a Group x Mode
interaction for mean RT latency, F (1, 34) = 4.7, p = 0.038, ƞp2 = 0.12. Decomposition of this
interaction manifested no statistically significant findings for either Group, t’s (34) ≤ 1.6, p’s ≥
0.1, drm ≤ 0.26, 95% CId [-0.07, 0.59], or Mode, t’s (17) ≤ 0.8, p’s ≥ 0.4, ds ≤ 0.28, 95% CId [0.38, 0.93]. No statistically significant findings were observed for change in response accuracy
from Pretest to Posttest, F’s (1, 34) ≤ 0.8, p’s ≥ 0.4, ƞp2’s ≤ 0.02.

34

CHAPTER 5
Discussion
Currently, only one other study has attempted to assess the effects of physical activity on
cognitive function in individuals with ASD, in which researchers observed improved
performance relative to working memory (Digit Span Tasks) and trending effects for inhibition
(Stroop Task; Anderson-Hanley, Tureck, & Schneiderman, 2011). However, this study is the
first to utilize an acute aerobic exercise based paradigm to specifically explore modulations in
behavioral indices of interference control before-and-after completion of the experimental
conditions. Findings replicate previous work indicating poorer response accuracy relative to a
modified flanker task for individuals diagnosed with ASD, compared to their typically
developing matched-controls (Adams & Jarrold, 2012; Keehn et al., 2010; Lopez et al., 2005)
Additionally, the physical activity condition showed no detrimental effects on interference
control for either group, corresponding with previous acute physical activity literature and
supporting the initial hypothesis for this study (Hillman, Pontifex, et al., 2009; Pontifex et al.,
2013; Drollette et al., 2012; Drollette et al, 2014). However, contrary to the remaining
hypotheses for this project, physical activity did not enhance task performance measures of
interference control in either group. This outcome may be the result of several factors including:
1) the physical activity intensity, 2) the study design, 3) small observed effect sizes, and 4)
sensitivity of outcome measures. While it is still unclear if acute aerobic physical activity may
have an impact on interference control in individuals with ASD, given this is the first venture to
explore this line of work valuable insight has been obtained that may help to strengthen future
work within this area.

35

Task Performance
Flanker task check.
Prior research has shown changes in task performance specific to reaction time and
response accuracy when completing incongruent trials of the modified flanker task relative to the
congruent trials (Eriksen & Eriksen, 1974; Hillman et al., 2003; Pontifex et al., 2013).
Specifically, slower reaction time was observed for the incongruent trials relative to the
congruent trials, indicating an increase in processing time during trials requiring a greater level
of interference control (Eriksen & Eriksen, 1974; Spencer & Coles, 1999). Poorer response
accuracy was also present for incongruent trials relative to congruent for both groups, suggesting
increased difficulty ignoring task irrelevant information corresponding with elevated stimuli
interference during incongruent trials (Spencer & Coles, 1999). While these findings are
standard for this task, they do serve as a practical check for the effective implementation of the
task.
Reaction time.
Within this investigation, individuals with ASD displayed decreased reaction time
latency at pretest during their exercise session relative to their rest session. While this finding is
not unique to the physical activity and cognition research, it has helped to elucidate some
methodological limitations of the current study that should be rectified in future work,
particularly when to disclose experimental condition assignment to participants. Transparency of
session order was maintained with parents in order to help them better prepare the children for
either the exercise session (i.e., wear sneakers and work out clothing to session) or the reading
session (i.e., ensure the participant brought reading material to the session if desired). However,
this approach may have had an adverse effect on the findings as many parents appeared to

36

heavily emphasize the physical activity component with their child in hopes of preparing them
for the treadmill on the day they were to complete the exercise condition. As a result, when the
children entered the lab they were prepared to immediately begin the treadmill activity, leading
to a high number of impulsive responses during the cognitive task as they attempted to ‘get to the
fun part’. In order to rectify these responses, the task was restarted, and they were informed why
we had to begin the task again. As the research team would instruct them to answer as
accurately and quickly as possible, participants may have emphasized accuracy over response
time, thus accounting for the observed reduction in reaction time during the pretest exercise
condition.
Future studies may consider altering the task utilized for the control condition, changing
from a seated reading protocol to an active control. During an active control condition,
participants would engage in an extremely low-intensity exercise intensity (i.e., walking on a
treadmill at 0.5 mph and zero percent grade) designed to avoid a meaningful increase in
physiological exertion. Addition of this design as a control condition would provide equal
opportunity for participants to engage in a treadmill based condition during either testing session,
therefore helping to alleviate any bias toward a particular condition based solely on the inclusion
of treadmill-based activity. Further, as it is feasible to rapidly process a participant’s pretest
performance, a pseudo-randomization for session order based on pretest performance during the
initial session may be utilized to eliminate pretest differences between conditions in later
analyses.
Response accuracy.
Modulations of response accuracy performance have been a consistent finding throughout
the physical activity and cognition literature, specifically for children (Drollette et al., 2014;

37

Drollette et al., 2012; Hillman et al., 2003; Pontifex et al., 2013). It has been suggested that
within a preadolescent population measurements of reaction time may demonstrate a
maintenance effect across congruency types due to a predisposition to impulsive responses
(Christakou et al., 2009), whereas adults tend to favor increased accuracy over speed and will
therefore slow their responses during the more challenging trial types (Davidson et al., 2006;
Drollette et al., 2014). Therefore, response accuracy has often been identified as a more reliable
indicator of physical activity influences on cognition in children. In accordance with previous
research, it was hypothesized in this study that individuals with ASD would present with poorer
response accuracy overall relative to their typically developing peers. This hypothesis was
supported within the data, adding to a well-established literature based examining differences in
cognitive control for individuals with ASD. However, no physical activity related effects were
observed, suggesting that for this population, physical activity may not influence inhibitory
control, a finding inconsistent with similar research in typically developing individuals and other
populations with developmental disorders. The data does, however, trend in accordance with
previous literature and replicate results for the TD group when assessed using planned contrast
comparisons designed to replicate posttest comparisons utilized in previous research (t (17) = 2.5, p = 0.021, drm = 0.23, 95% CId [0.03, 0.43]). This trend and consistency with other study
designs, indicate that while findings are not significant there may be underlying components that
are contributing to this lack of a finding. One possibility is that this discrepancy may be the
result of intensity utilized within each experimental condition. Research has established that,
much like the inverted-U perspective, modulations in cognition are at their greatest following
moderate-to-vigorous activity (assessed through heart rate based intensity for exercise), with
diminished effects for cognitive control at vigorous and low intensity levels (Bender &

38

McGlynn, 1976; Davey, 1973; Hillman et al., 2012; Weingarten & Alexander, 1970). Utilizing
this principle, this study aimed to have participants complete the exercise condition between 65%
and 75% of their age-predicted heart rate max. However, upon analysis of heart rate intensities
following exercise both groups fell below that threshold (TD: 62.4 ± 0.8%; ASD: 62.1 ± 1.1%).
While the groups are not significantly below the lower bound associated with moderate intensity
activity, the fact that they are outside of this range may influence the potential for physical
activity influences on interference control. Additionally, the TD sample does present with above
average IQ (112.7 ± 17.0) potentially effecting performance on the cognitive task in which
participants may have experienced a ceiling effect between pretest and posttest for each
experimental condition.
Interference & change scores.
Although a congruency effect was observed for reaction time and response accuracy,
secondary analysis of interference scores collapsed across congruencies yielded no statistically
significant effects. As interference scores are intended to reflect the time difference needed for
handling the added interference of an incongruent trial and the ability to effectively manage that
interference in order to initiate a correct response (Buck, Hillman, & Castelli, 2008), this finding
would suggest that regardless of group, experimental condition, and time point participants
experienced no improvements, and just as important no detriments, to the regulation of these
processes. Similar findings were also found when controlling for change from pre-to posttest. In
this analysis, the change score is intended to represent the improvement (or deterioration) of a
particular variable with the goal in this study of determining if the changes observed for each
condition varied within and between the groups. Results from this assessment indicated that

39

neither condition within the groups, or conditions between the groups varied significantly from
one another.
Practical Implications
Although this study provided limited statistical insight into the effects of acute physical
activity on interference control, the contribution of this work to the field is strongly rooted in the
conceptual significance of the overall methodology. Research exploring the cognitive abilities of
those diagnosed with ASD has often been seen as challenging, and improbable, due to the
various possible expression of the disorder for each individual (Smith et al., 2007). This has
resulted in a limited body of research to explore, and within that scope, an inconsistent usage of
cognitive tasks across studies and low sample sizes. These limitations of the research area are
not to be discredited, however, as these outcomes clearly reflect the struggles of working with an
ASD population. It is understandable, given these challenges, that to date this is only the second
project to explore this research question (Anderson-Hanley et al., 2011), and the first to
implement this treadmill-based design using modernized tasks for interference control. Germane
to the contributions of the project, however, is the support for feasible implementation of the
study design within an ASD population. With much of the research examining physical activity
in those with ASD utilizing alternative activity programs (i.e., karate, horseback riding, rock
climbing, swimming, and anaerobic training; (Scharoun, Wright, Robertson-Wilson, Fletcher, &
Bryden, 2017), the practicality of utilizing a treadmill modality for this study was concerning
(although this is the common mode of physical activity used within the physical activity and
cognition literature). However, participants displayed high competency for completing the
walking task, indicating that inclusion of treadmill-based activities within this area of study is
possible. In addition to the mode of activity utilized, there have been concerns regarding the

40

effect to which known fine motor deficits in individuals with ASD may impact performance on
the cognitive task (Provost, Lopez, & Heimerl, 2006). While fine motor skills were not assessed
in this study, the impact of these deficits in relation to the cognitive task can be observed through
reaction time latency. Therefore, as no between-group effects were observed for reaction time (F
(1, 34)

= 0.2, p = 0.7, ƞp2 = 0.01), use of this cognitive task format does not appear to be influenced

by fine motor deficits for individuals with ASD. However, further examination of this matter is
needed in order to fully elucidate if, and to what extent, fine motor impairment in individuals
with ASD may affect their ability to perform the required movements to complete the cognitive
task.
Additionally, while the use of multi-day studies when working with individuals with
diagnosed ASD is not uncommon, the study design utilized within this project provided
beneficial insight into recruitment, participant attrition and participant interest. In recent years
the use of a repeated measures within-subjects design has been suggested as the default study
design for exploring physical activity effects on cognitive function. This design structure is
beneficial in that it accounts for individual variability in the model and includes a control
comparison (Pontifex et al., 2015), however it may be unsuitable for this population. While the
overall time commitment for the study was ~4 hours (spread across three sessions), the added
burden of attending multiple sessions was difficult for many families. Due to other
commitments, such as school, therapy (behavioral, physical, occupational, and speech), work,
and family, it was often difficult for families to identify multiple time gaps within their rigorous
schedules. To overcome this burden on the participant and their families, many of the successful
multi-day research studies have altered this challenge into a researcher burden by taking the
study out of the lab and to the participants (MacDonald et al., 2012; Reynolds, Pitchford, Hauck,

41

Ketcheson, & Ulrich, 2016). Alternatively, a between-subject’s pre-posttest design over a single
laboratory session (Ferris et al., 2007; Magnié et al., 2000; Nakamura et al., 1999; Yagi et al.,
1999) may also help to ease this participant load. Ultimately, the inclusion of either approach
may be beneficial in future work in order to ensure low participant burden and ample sample size
for detecting effects on cognition.
Limitations & Future Directions
The primary goal of the study was to elucidate the potential effects of acute aerobic
physical activity on the interference aspect of inhibitory control within individuals diagnosed
with ASD. While there were subtle findings associated with the results of this endeavor, the
project did ultimately fail to find significant effects of physical activity on interference control in
either group studied. Such a finding may be driven by a number of factors, most notably the age
range of the participants. Although participants were matched based on age, the range of
participant age varied from 6.8 to 22.2 years old. With research indicating that age may act to
moderate the performance of individuals on various cognitive control tasks (Hillman et al.,
2008), and that deficits associated with cognitive control may diminish with age among
individuals with ASD (Happé, Booth, Charlton, & Hughes, 2006), inclusion of such a broad age
range may act to mask potential effects of physical activity on cognition. Additionally, this age
range encompasses a time of significant physical and cognitive development that may impact the
neurological processing and physiological capabilities for an individual (Petersen, 1988). Future
research should narrow the age gap by separating groups into young adults, adolescents, and
preadolescents in order to help minimize the effect of this potential confound.
Another contributing factor may be associated with the low heart rate intensities observed
in this study during the exercise condition. As discussed above, heart rate intensity provides a

42

relatively easy index for assessing the potential benefits of physical activity on cognitive control
(Chang et al., 2012; Hillman et al., 2012; Lambourne & Tomporowski, 2010), however within
this study both groups fell below the moderate intensity classification associated with the
greatest modulation in cognition. The failure to meet this minimum threshold may result from
two distinct factors: 1) inclusion of an aerobically fit population overall, 2) intensity thresholds
based on age-predicted HR maximum (220 minus age), and 3) concerns for participant safety.
Without measures of aerobic fitness, it is difficult to discern the impact of the first possible
factor, however participant level of physical activity involvement was included within the health
history demographic survey. Participants (or their parents) reported that on average they
engaged in 2 hours of physical activity per day, with the minimum report indicating at least 1
hour per day. While these findings may seem insignificant, based on these reports this sample of
participants is at least meeting, and with many exceeding, the physical activity guidelines
suggested for kids. This conclusion would suggest that these individuals may be aerobically fit,
thus impacting their heart rate based intensity levels. As this study did not perform a standard
assessment for maximum aerobic capacity (VO2max) in which maximum HR can be obtained
directly, an age-predicted HR max was utilized to determine HR intensity levels. This method of
determining intensity level, known as the zero to peak method (Karvonen & Vuorimaa, 1988), is
a common protocol used throughout physical activity literature, however there are disadvantages
that could have impacted the ability for the participants in this study to reach the minimum HR
intensity threshold. In particular, inclusion of age-predicted HR max in this method inherently
adds a large amount of standard error to the outcome as it does not account for individual
variability between participants. Additionally, because this overall method does not account for

43

variability in cardiac responses between individuals it is possible that the HR intensity thresholds
used do not accurately reflect the levels of the participants due to their fitness level.
The concerns for participant safety stem from this potential impact on heart rate based
intensity level, as participants significantly varied in the speed and grade used during the
exercise condition. Of particular interest was a significant decrease in speed and grade observed
within the ASD group while completing the exercise condition. Participants in this group often
utilized a varied gait pattern when walking on the treadmill that would result in a smaller stride
length and a greater frequency of steps. This observation is consistent with gait based research
in children with ASD (see Kindregan, Gallagher, & Gormley, 2015 for review), as children with
ASD have reduced range of motion in the ankle and knee joints. Interestingly, this altered gait
would result in a heart rate just at the 65% threshold, however with adjustments to the workload
their heart rate would exhibit little variation. In spite of this limited increase in heart rate,
workload was often left steady after this mild fluctuation due to researcher concern for
participant safety and the observed gait pattern. Future research may find that implementing an
alternative mode of exercise, such as a cycle ergometer may allow for improved heart rate
measure of intensity. However, as riding a standard two-wheel bicycle is a motor skill that
individuals with ASD often struggle with (MacDonald et al., 2012; Reynolds et al., 2016), this
mode may demonstrate alternative motor difficulties, therefore an alternative to adjusting mode
of activity may be to alter the measure of intensity (i.e., Aerobic Capacity [VO2] and/or Lactate
Threshold).
A final contributing factor to the outcomes from this study may be the overall sample
size. With research indicating moderate-to-large effect sizes (0.4 to 0.9) relative to the effects of
acute exercise on aspects of cognitive control, a conservative estimate (0.5) was utilized for a

44

priori power analyses to determine sample size for this investigation. With an observed effect
size for response accuracy of 0.33 in the TD group and 0.16 for the ASD group, this preliminary
appraisal is a clear overestimation. Therefore, the study overall may simply be underpowered
given the small effect sizes observed, suggested that future work in this area may need a larger
sample size in order to observe effects within individuals diagnosed with ASD compared to
previous work in other populations.
Conclusion
Interventions examining the effects of physical activity on cognitive control in
individuals with ASD are rare, and relatively new concepts. The current study utilized an acute
aerobic physical activity paradigm shown to influence the interference aspect of inhibition, in an
attempt to better understand the potential for cognitive improvement within the ASD population.
Despite finding support for impaired interference control in individuals with ASD compared to
their typically developing matched-controls, an effect for physical activity was not observed,
therefore it remains unclear at this time if, and to what degree, physical activity may influence
cognition in individuals diagnosed with ASD.
With respect to the limitations of this project, there are still a number of future directions
to explore. While inhibition has been the core aspect of cognitive control linked to ASD
symptomologies, examining the effect of physical activity on other components of cognitive
control may be beneficial. This study opted to focus on inhibition, specifically interference
control, as previous research has observed deficits in inhibition for those with ASD and acute
aerobic physical activity has been shown to modulate this aspect of cognition. Results from this
study suggest that it may be possible that physical activity does not affect inhibition within this
population. However, individuals with ASD have also been observed to present with additional

45

deficits in set-shifting and working memory. As physical activity has been shown to affect these
aspects of cognitive control, exploration into the modulations of these components in response to
physical activity may provide greater insight into the effects of physical activity on cognitive
control within individuals with ASD. Additionally, behavioral measures of cognitive control
may not be sensitive enough measures to truly elucidate the influence of physical activity.
Therefore, future research should consider implementing neuroimaging techniques, such as
electroencephalographic measures of event-related potentials to assess neuroelectric indices of
inhibition and functional magnetic resonance imaging to measure changes in activity throughout
different regions of the brain, to assess the effects of physical activity on brain function relative
to cognitive control.
Despite the limitations of this study, the overall contribution for future work is still
significant. As this is only the second study to explore the effects of acute physical activity on
cognition in individuals with ASD, identification of the various methodological limitations
observed throughout this study should serve to greatly strengthen the overall design of future
projects. The addition of observed effects sizes for this population relative to physical activity
influences on cognition, will also help to ensure ample power for future studies; thus, helping to
bolster the literature base as a whole. Finally, support for the feasibility of the current protocols
within this population will hopefully provide the means for other researchers to begin exploring
this area. As this line of research has the potential to impact public health issues relative to
childhood inactivity, educational policy, and overall quality of life for individuals diagnosed with
ASD, these contributions may provide a foundation for future work within this area.

46

APPENDICES

47

Appendix A: IRB Approval Letter

Figure 6.1. Copy of IRB approval letter.

48

Appendix B: Dissertation Funding Sources

Dissertation Funding Sources

1. Dissertation Completion Fellowship - 2017
College of Education, Michigan State University
Funded - $7,000
Use: Study coordinator assistantship support
2. Summer Research Renewable Fellowship – 2015/2016
College of Education, Michigan State University
Funded - $12,000
Use: Study coordinator assistantship support

3. Research Practicum/Research Development Fellowship - 2013
Department of Kinesiology, Michigan State University
Funded - $3,340
Use: Participant compensation and supplies

Not Funded
1. Research Practicum/Research Development Fellowship – 2014
Department of Kinesiology, Michigan State University
Unfunded - $4,000

49

Appendix C: Informed Assent – Age 5-7

Figure 6.2. Informed assent paperwork for children 5 to 7 years old.

50

Appendix D: Informed Assent – Age 8-12

Figure 6.3. Informed assent paperwork for children 8 to 12 years old.

51

Figure 6.3 (cont’d).

52

Appendix E: Informed Assent – Age 13-17

Figure 6.4. Informed assent paperwork for children 13 to 17 years old.

53

Figure 6.4 (cont’d).

54

Figure 6.4 (cont’d).

55

Appendix F: Informed Consent – Age 18+

Figure 6.5. Informed consent paperwork for adults 18 years old and older.

56

Figure 6.5 (cont’d).

57

Figure 6.5 (cont’d)

58

Appendix G: Informed Consent - Parent

Figure 6.6. Informed consent paperwork for parents of children under 18 years old.

59

Figure 6.6 (cont’d).

60

Figure 6.6 (cont’d).

61

Figure 6.6 (cont’d).

62

Appendix H: Recruitment Flyer for Individuals with ASD

Figure 6.7. Recruitment flyer for individuals with ASD.

63

Appendix I: Recruitment Email for Individuals with ASD

Subject: Research Study for Individuals with Autism Spectrum Disorder (Age 5-25)
Email
Hello,
The Health Behaviors and Cognition Lab at Michigan State University is recruiting Individuals
with Autism Spectrum Disorder aged 5-25 who are interested in helping us study the link
between cognition and exercise.
Where does this study take place?
Participation takes place at Michigan State University, with easily accessible parking.
What will you be asked to do?
Individuals who participate will be asked to go through a screening session on the first day.
Those who qualify will be asked to participate in two additional sessions where we will have you
either walk on a treadmill or rest while reading a book. Participants will also be asked to play
some brief computerized games before and after the reading or activity. Each session lasts 60 to
90 minutes and can be done whenever works best for your schedule (after school, evenings,
weekends, early-out school days, we will make it work!).
How will you be compensated?
Participants will be compensated $50 for completing the study (~$10/hr.)
How can we get involved?
If you are interested in participating or have any questions please contact Drew at (517) 3530892 or HBCL@msu.edu.
With your help we can work together to better understand how healthy bodies result in healthy
brains.
Drew Parks, M.S.
Doctoral Candidate I Graduate Assistant
Health Behaviors and Cognition Laboratory
Department of Kinesiology
Michigan State University
38 IM Sports Circle
308 W. Circle Drive
East Lansing, MI 48824
(517)433-0892 (Office/Lab)
(517)353-2944 (Fax)
http://education.msu.edu/kin/hbcl

64

Appendix J: Recruitment Flyer for TD Individuals

Figure 6.8. Recruitment flyer for typically developing individuals..

65

Appendix K: Recruitment Email for TD Individuals
Subject: Physical Activity and Cognition Study
Email
Hello,
The Health Behaviors and Cognition Lab at Michigan State University is recruiting individuals
age 14-18 who are interested in helping us study the link between cognition and exercise. Our
work has recently been gaining attention on Facebook, in the New York Times, the Wall Street
Journal, and was even mentioned on the Today Show; but we still need more participants for our
ongoing studies.
Where does this study take place?
Participation takes place at Michigan State University, with easily accessible parking.
What will my child be asked to do?
Individuals who participate will be asked to complete two testing sessions, each approximately
60-90 minutes in duration. During the sessions participants will be asked to play some brief
computerized games before and after either walking on a treadmill or reading a book. Sessions
can be done whenever works best for your schedule (daytime, evenings, weekends, early-out
school days, we will make it work!).
How will my child be compensated?
Children will be compensated $50 for completing the study (~$10/hr.)
How can we get involved?
If you are interested in participating or have any questions please contact Drew at (517) 3530892 or HBCL@msu.edu.
With your help we can work together to better understand how healthy bodies result in healthy
brains.
Drew Parks, M.S.
Doctoral Candidate I Graduate Assistant
Health Behaviors and Cognition Laboratory
Department of Kinesiology
Michigan State University
38 IM Sports Circle
308 W. Circle Drive
East Lansing, MI 48824
(517)433-0892 (Office/Lab)
(517)353-2944 (Fax)
http://education.msu.edu/kin/hbcl

66

Appendix L: SNAP-IV

Figure 6.9. SNAP-IV assessment for expression of ADHD symptoms.

67

Figure 6.9 (cont’d).

68

Appendix M: Social Communication Questionnaire (SCQ)

Figure 6.10. Social communication questionnaire assessment.

69

Figure 6.10 (cont’d).

70

Appendix N: Physical Activity Readiness Questionnaire (PAR-Q)

Figure 6.11. Physical activity readiness questionnaire.

71

Appendix O: Health History Demographic Survey

Figure 6.12. Health history demographic form completed online.

72

Figure 6.12 (cont’d).

73

Figure 6.12 (cont’d).

74

Figure 6.12 (cont’d).

75

Figure 6.12 (cont’d).

76

Figure 6.12 (cont’d).

77

Figure 6.12 (cont’d).

78

Figure 6.12 (cont’d).

79

Figure 6.12 (cont’d).

80

Figure 6.12 (cont’d).

81

Figure 6.12 (cont’d).

82

Figure 6.12 (cont’d).

83

Figure 6.12 (cont’d).

84

Figure 6.12 (cont’d).

85

Figure 6.12 (cont’d).

86

Figure 6.12 (cont’d).

87

Figure 6.12 (cont’d).

88

Appendix P: Tables for Results Section
Table 6.1.
Participant demographic values (Mean ± SD).
Measure

ASD

TD

N

18 (0 female)

18 (4 female)

6

8

12.7 ± 4.2

12.3 ± 4.7

Pre-pubescent (Stage 1)

6

9

Initial development (Stage 2)

4

3

Continued development (Stages 3 & 4)

3

2

Post-Pubescent (Stage 5)

5

4

97.4 ± 18.2*

112.7 ± 17.0*

18

18

6.2 ± 4.1

6.6 ± 4.4

Inattention subscale

1.6 ± 0.7**

0.7 ± 0.6**

Hyperactivity/Impulsivity subscale

1.4 ± 0.6**

0.4 ± 0.4**

Combined subscale

1.5 ± 0.6**

0.5 ± 0.4**

Social Communication Questionnaire (SCQ)

15.2 ± 4.8**

6.0 ± 3.7**

Age-Predicted HRmax (bpm)

207.3 ± 4.2

207.7 ± 4.7

Weekday (hours/day)

1.8 ± 1.0

2.8 ± 1.6

Weekend (hours/day)

2.2 ± 1.0

2.6 ± 1.1

Non-White
Age (years)
Tanner stage

WASI-II Composite (IQ)
Socioeconomic status (SES)
Middle SES
Education (Grade)
SNAP-IV

Physical Activity Engagement

Note: WASI-II – Full Scale-2 composite, utilizing vocabulary and matrix reasoning sub-tests.
Socioeconomic status – Low = 1, Middle = 2, & High = 3. SNAP-IV measures – mean scores on
each respective subset of questions based on parent report. Social Communication Questionnaire
– total score based on parent report. Age-Predicted HRmax – calculated using 220 – participant
age. *p ≤ 0.05; **p ≤ 0.001.

89

Table 6.2.
Ranges for participant demographics.
Measure

ASD

TD

Age (years)

[6.8, 22.0]

[7.3, 22.2]

WASI-II Composite (IQ)

[62.0, 125.0]

[88.0, 147.0]

Education (Grade)

[0.0, 14.0]

[2.0, 16.0]

Inattention subscale

[0.4, 2.6]

[0.0, 1.7]

Hyperactivity/Impulsivity subscale

[0.1, 2.2]

[0.0, 1.1]

Combined subscale

[0.4, 2.3]

[0.0, 1.3]

Social Communication Questionnaire (SCQ)

[8.0, 23.0]

[0.0, 12.0]

Age-Predicted HRmax (bpm)

[198.0, 213.2]

[197.8, 212.7]

Weekday (hours/day)

[1.0, 5.0]

[1.0, 8.0]

Weekend (hours/day)

[1.0, 5.0]

[1.0, 5.0]

SNAP-IV

Physical Activity Engagement

Note: WASI-II – Full Scale-2 composite, utilizing vocabulary and matrix reasoning sub-tests.
Socioeconomic status – Low = 1, Middle = 2, & High = 3. SNAP-IV measures – mean scores on
each respective subset of questions based on parent report. Social Communication Questionnaire
– total score based on parent report. Age-Predicted HRmax – calculated using 220 – participant
age. *p ≤ 0.05; **p ≤ 0.001.

90

Table 6.3.
Clinical status confirmation for the ASD group (Mean ± SD).
Measure

ASD

ADOS Module 3
N

12

Overall Total (SA + RRB)

12.3 ± 4.4

Social Affect (SA)

10.2 ± 4.1

Restricted and Repetitive Behavior (RRB)

2.2 ± 1.6

Comparison Score

7.0 ± 1.9

ADOS Module 4
N

6

Communication

3.8 ± 1.1

Social Interaction

6.2 ± 2.5

Imagination/Creativity

0.6 ± 0.5

Stereotyped Behaviors and Restricted Interests

1.8 ± 0.8

Note: All scores represent total scores for the respective assessment category.

91

Table 6.4.
Mean (± SD) Task Performance Characteristics.
Reaction Time (ms)

Response Accuracy (%)

ASD

TD

ASD

TD

Congruent

454.7 ± 143.3

445.7 ± 108.9

78.3 ± 18.9

83.5 ± 17.2

Incongruent

472.7 ± 150.9

480.3 ± 117.8

62.4 ± 20.9

76.1 ± 16.4

Congruent

462.6 ± 154.2

439.6 ± 107.2

78.6 ± 19.7

84.4 ± 17.9

Incongruent

490.0 ± 158.4

480.5 ± 119.9

62.0 ± 23.0

78.4 ± 19.0

Congruent

487.0 ± 146.6

444.0 ± 100.9

80.3 ± 15.8

87.1 ± 13.2

Incongruent

504.5 ± 151.1

476.7 ± 114.9

62.7 ± 23.4

77.5 ± 16.5

Congruent

479.2 ± 164.4

455.5 ± 113.3

78.8 ± 18.7

88.7 ± 14.6

Incongruent

500.8 ± 144.9

485.4 ± 122.0

61.8 ± 21.8

80.3 ± 16.3

Condition
Rest – Pretest

Rest – Posttest

Exercise – Pretest

Exercise – Posttest

92

Appendix Q: Figures for Results Section

Figure 6.13. Mean HR (± SE) for each group across experimental condition.

93

Figure 6.14. Mean (± SE) RT latency for (A) congruent and (B) incongruent trials for each
condition by group.

94

Figure 6.15. Mean (± SE) response accuracy for (A) congruent and (B) incongruent trials for
each condition by group.

95

Figure 6.16. Mean (± SE) interference score for (A) reaction time latency and (B) response
accuracy for each condition by group.

96

Figure 6.17. Dot plots for reaction time latency at pretest, posttest, and change score for each
condition by group.

97

Figure 6.18. Dot plots for response accuracy at pretest, posttest, and change score for each
condition by group.

98

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