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Manipulating cognitive intensity during aerobic exercise: clinical proof of concept

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Content Manipulating Cognitive Intensity During Aerobic Exercise: Clinical Proof of Concept
by
Malcolm J. Jones
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOKINESIOLOGY)
May 2024

Manipulating Cognitive Intensity During Aerobic Exercise
ii
DEDICATION
This dissertation is dedicated to the courageous and determined individuals
who have overcome challenging obstacles and remained committed to their
aspirations. Your resilience and persistence have inspired me to pursue my
own academic and personal goals with unwavering determination. Thank
you for showing me the power of a positive attitude in the face of adversity.

Manipulating Cognitive Intensity During Aerobic Exercise
iii
ACKNOWLEDGEMENTS
“Real education means to inspire people to live more abundantly, to learn to
begin with life as they find it and make it better.”
- Dr. Carter G. Woodson (1875 – 1950)
Carter G. Woodson, widely known as the father of Black History Month, firmly believed
that true education goes beyond the mere acquisition of knowledge, and instead aims to inspire
individuals to lead more meaningful and rewarding lives. For Woodson, education is about
empowering people to work with what they have, improve their current situation, and create a
brighter future for themselves and their community. The above quote perfectly encapsulates why
I consider the Division of Biokinesiology to be exceptional. The faculty and staff in our halls
exemplify this educational philosophy and continue to inspire generations of learners and
educators to strive for excellence and use their knowledge to make a positive impact on the
world. I express my deep gratitude and appreciation for this legacy and extend my heartfelt
thanks to those who have helped me embrace this philosophy.
I am immensely grateful to Dr. Gordon for his constant words of encouragement and
willingness to meet with students. His support made the Division feel like home. I extend my
thanks to all the faculty in the Division who provided me with the resources and opportunities to
thrive. I also express my special thanks to my dissertation committee and co-investigators, Dr.
James Finley, Dr. Beth Fisher, Dr. E. Todd Schroeder, Dr. Susan Sigward, and Dr. Judy Pa. To
my advisor, Dr. E. Todd Schroeder, you have shown me that effective work need not be dull or
tedious. When I have my own lab one day, I hope it is as enjoyable and productive as the
Clinical Exercise Research Center (CERC) under your guidance. Thank you for making research
an enjoyable experience and for showing me that I do not have to pretend to be someone else to
be a good physiologist.

Manipulating Cognitive Intensity During Aerobic Exercise
iv
I extend my gratitude to Dr. Dominguez and Dr. Erceg for teaching me so much in
CERC. Dr. Matthews, thank you for your efforts to create an inclusive environment within the
Division. They are working, and I am a testament to that. Dr. Kulig, your class in the first
semester was undoubtedly the toughest class I have ever taken in my life. However, your kind
spirit and genuine enthusiasm for knowledge made learning under you a pleasant experience.
Thank you for introducing me to some of the most important people in your life, and I look
forward to learning from you for a long time. Dr. Resnik, thank you for leading by example. The
volunteer work you do in the community, in addition to your academic work, is inspiring. The
example you set for us as educators to support the community is the reason why I began
incorporating community service into my research endeavors. Dr. Poppert, your dedication and
care for your patients and students are truly exceptional. The passion you put into your work is
inspiring, and I am grateful for the knowledge and wisdom you imparted to me. I express my
heartfelt appreciation to Dr. Leech and Dr. Kutch for their unwavering support and constant
feedback to strengthen my research. Although neither of them was my direct professor, they
went out of their way to provide me with guidance.
I would also like to express my gratitude to my family and classmates for their
unwavering support throughout my academic journey. To my family, thank you for always
believing in me and encouraging me to pursue my dreams. Your love and support have been my
rock, and I could not have done this without you. To my classmates, thank you for pushing me to
be my best self and for being my support system. Your collaboration, encouragement, and
camaraderie have made this experience truly unforgettable. To my classmates, thank you for the
memories and the many hours spent studying together. Your encouragement and support have

Manipulating Cognitive Intensity During Aerobic Exercise
v
been a lifeline throughout this journey, and I am grateful to have shared this experience with you.
I will cherish the friendships and connections we have made for years to come.
Finally, I extend my thanks to the unsung heroes of the Division, Ramraj Singh, Oshawa
Smith, Janet Stevenson, Troy Lord, Barbara Roddy, Trinh Nguyen, Anthony Wallace, Sadi
Metz, and Matthew Sandusky. You are the foundation of our program and have helped every
student who has come through the Division avoid dozens of breakdowns. Your contributions are
invaluable, and I am grateful for everything you do.

Manipulating Cognitive Intensity During Aerobic Exercise
vi
TABLE OF CONTENTS
DEDICATION ................................................................................................................................. ii
ACKNOWLEDGEMENTS ............................................................................................................iii
LIST OF TABLES ........................................................................................................................viii
LIST OF FIGURES......................................................................................................................... ix
ABSTRACT.................................................................................................................................... xi
CHAPTER I: AIMS ......................................................................................................................... 1
CHAPTER II: BACKGROUND & SIGNIFICANCE..................................................................... 4
Statement of the Problem ............................................................................................................... 4
Benefits of Cognitive + Aerobic Training....................................................................................... 5
Potential Mechanisms..................................................................................................................... 6
Preliminary Information ................................................................................................................ 7
CHAPTER III: LITERATURE REVIEW ..................................................................................... 27
PART 1: NORMAL COGNITIVE AGING ................................................................................. 27
Introduction..................................................................................................................................................... 27
Cognition and Aging ....................................................................................................................................... 28
Analysis of Specific Cognitive Domain Changes with Age .......................................................................... 33
Individual Differences in NCA....................................................................................................................... 38
Conclusion........................................................................................................................................................ 40
PART 2: EXERCISE EFFECT ON COGNITIVE AGING......................................................... 41
Introduction..................................................................................................................................................... 41
PART 3: COGNITIVE + AEROBIC EXERCISE INTERVENTION......................................... 50
Introduction..................................................................................................................................................... 50
Executive Function.......................................................................................................................................... 51
EF and Aerobic Training................................................................................................................................ 52
EF and Cognitive Training............................................................................................................................. 52
Maximizing EF through Training.................................................................................................................. 53
VR Exergaming Interventions ....................................................................................................................... 53
Limitations....................................................................................................................................................... 54

Manipulating Cognitive Intensity During Aerobic Exercise
vii
CHAPTER IV: COGNITIVE INTENSITY CAN BE SYSTEMATICALLY
MANIPULATED DURING AEROBIC EXERCISE………………………………………….. 56
ABSTRACT.................................................................................................................................. 56
INTRODUCTION........................................................................................................................ 57
PURPOSE .................................................................................................................................... 59
METHODS/DISSERTATION APPROACH ............................................................................... 61
Participants and Study Design.......................................................................................................................... 61
Intervention Procedures.................................................................................................................................... 64
Primary Outcomes ............................................................................................................................................ 65
Secondary Outcomes......................................................................................................................................... 66
CHAPTER V: SUMMARY & CONCLUSIONS.......................................................................... 92
REFERENCES............................................................................................................................... 94

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viii
LIST OF TABLES
Table 1. Participant Descriptive Data ........................................................................................... 20
Table 2. Traditional vs Digital WCST64 Score by Group............................................................ 20
Table 3. Digital WCST64 Scores for Each Condition by Group.................................................. 21
Table 4. Post-Test Fatigue Data Assessed by 10 Meter Walk Test.............................................. 24
Table 5. Outlined Literature Review of Aerobic + Cognitive Clinical Interventions to Improve
Cognition ...................................................................................................................................... 55
Table 6. Demographic and socio-economics characteristics....................................................... 69
Table 7. The distribution and difference between traditional and digital WCST64 using paired
t-test………………………………………………………………………………………………70
Table 8. The distribution and difference of error T-scores between traditional and digital
WCST64 by age group.................................................................................................................. 71
Table 9. The distribution and difference of digital WCST64 performance scores in time,
complexity, and age group at stationary ....................................................................................... 72
Table 10. The distribution and difference of digital WCST64 performance scores in age and
complexity by time ....................................................................................................................... 72
Table 11. The distribution and difference of digital WCST64 performance scores in
complexity by age………………………………………………………………………………..72
Table 12. The distribution and difference of digital WCST64 performance scores in task (dual
task walking versus single task standing), complexity, time, and age.......................................... 75
Table 13. The distribution and difference of digital WCST64 performance scores in time,
complexity, and age by task.......................................................................................................... 75
Table 14. The distribution and difference of digital WCST64 performance scores in
complexity by age………………………………………………………………………………..75
Table 15. The distribution and difference of digital WCST64 performance scores in
complexity, time, and age for walking…………………………………………………………...76
Table 16. The distribution and difference of digital WCST64 performance
scores in complexity and age by time……………………………………………………………77
Table 17. The distribution and difference of digital WCST64 performance scores in
complexity by age for walking…………………………………………………………………..77
Table 18. Difference of the means of stride length and gait speed across conditions…………...79
Table 19. Multiple pairwise comparisons of conditions on the mean of stride length (mm)……79
Table 20. Multiple pairwise comparisons of conditions on the mean of gait speed (m/s)………80
Table 21. The distribution and difference of the mean of stride length across conditions by
age group…………………………………………………………………………………………80
Table 22. The distribution and difference of the mean of stride length across conditions by age
group ............................................................................................................................................. 82
Table 23. The distribution and difference in the mean of stride length (mm) and gait speed
(m/s) from each condition from baseline………………………………………………………...84

Manipulating Cognitive Intensity During Aerobic Exercise
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LIST OF FIGURES
Figure 1. Model of neuroplasticity induced by acute aerobic exercise……………………………7
Figure 2. Means ± standard deviation (SD) of traditional WCST64 and digital Blue Goji
WCST64…………………………………………………………………………………………13
Figure 3. Means ± standard deviation (SD) of first trial vs second trial of the digital
Blue Goji WCST64………………………………………………………………………………14
Figure 4. Means ± standard deviation (SD) of the digital Blue Goji WCST64 trials with
unlimited selection time vs 3 second selection time vs 2 second selection time………………...15
Figure 5. Means ± standard deviation (SD) of first trial vs second trial vs third trial of
the digital Blue Goji WCST64…………………………………………………………………...15
Figure 6. Means ± standard deviation (SD) of the Blue Goji digital WCST64 trials
with 3 sorting options vs 4 sorting options vs 5 sorting options vs 6 sorting options…………...16
Figure 7. Means ± standard deviation (SD) of first trial vs second trial vs third trial
vs fourth trial of the digital Blue Goji WCST64…………………………………………………16
Figure 8. Means ± standard deviation (SD) of first trial vs second trial vs third trial
vs fourth trial of the digital Blue Goji WCST64…………………………………………………17
Figure 9. Means ± standard deviation (SD) of first trial vs second trial of the digital Blue Goji
WCST64…………………………………………………………………………………………17
Figure 10. Visualization of WCST64 performance assessed by score using the traditional vs
digital delivery method by age group……………………………………………………………21
Figure 11. Visualization of WCST64 performance assessed by score on all interventions by
age group and looking at time constraint as a main effect……………………………………….22
Figure 12. Visualization of WCST64 performance assessed by score on all interventions by
age group and looking at complexity as a main effect…………………………………………...23
Figure 13. Visualization of WCST64 performance assessed by score on all interventions by
age group and looking at dual tasking as a main effect………………………………………….24
Figure 14. The shifting architecture of cognition across the adult life span……………………..29
Figure 15. Diagram of participant recruitment and testing………………………………………63
Figure 16. Summary on equivalence evaluation of digital WCST64……………………………70
Figure 17. The distribution of digital WCST64 performance score across conditions by age
group.…………………………………………………………………………………………….73
Figure 18. The distribution of digital WCST64 performance score across conditions by age
group (boxplot)…………………………………………………………………………………..74
Figure 19. The distribution of digital WCST64 performance scores across conditions at
walking by age group…………………………………………………………………………….77
Figure 20. The distribution of digital WCST64 performance scores across conditions at
walking by age group (boxplot)………………………………………………………………….78
Figure 21. The distribution of the mean of stride length across conditions by age group……….81
Figure 22. The distribution of the mean of stride length across conditions by age group
(boxplot)………………………………………………………………………………………….81
Figure 23. The distribution of the mean of gait speed across conditions by age group………….82
Figure 24. The distribution of the mean of gait speed across conditions by age group
(boxplot)………………………………………………………………………………………….83
Figure 25. The correlation of the percent change between the mean of stride length
and digital WCST performance score for each condition at walking from baseline..…………...85

Manipulating Cognitive Intensity During Aerobic Exercise
x
Figure 26. The correlation of the percent change between the mean of stride length and digital
WCST performance score for each condition at walking from baseline by age group………….86
Figure 27. The correlation of the percent change between mean of gait speed and digital……...86
Figure 28. The correlation of the percent change between mean of gait speed and digital……...87

Manipulating Cognitive Intensity During Aerobic Exercise
xi
ABSTRACT
Human lifespan has continually increased alongside a simultaneous rise in sedentary
lifestyle. While this manifests in a variety of physical complications that are often the point of
discussion, inactivity has shown to heighten age-associated cognitive decline, to which there is
not yet a medical treatment for. However, physical activity may provide a neuroplastic
environment that could curb such neurologic decline. Promising research has revealed that
physical activity induced adaptations in neuroplasticity may mediate cognitive improvements
through the facilitation of exercise-induced neurotrophic factors. Growing evidence supports the
idea of an experience-dependent neuroplasticity that requires an environment that is
simultaneously (1) cognitively engaging, promoting alteration of neural synapses
(synaptogenesis), and (2) aerobically challenging, facilitating improved circulatory function in
the brain (angiogenesis), both of which promote neurogenesis and improved cognitive function.
These types of interventions have not been optimized in humans because they are currently
investigated with exercise that does not have sensory-motor and cognitive procedures that can be
personalized. Specifically, while intensity of angiogenesis-related motor tasks can be
personalized through mechanisms such as increased speed, there is no standard procedure for the
personalized loading of synaptogenesis-related cognitive activity. This dissertation aims to
determine if executive function can be progressively loaded using multimedia delivery, time
constraint, and dual task environments as a first step to establish a systematic schema to
prescribe this type of intervention in an individualized manner. By utilizing the traditional
Wisconsin Card Sorting Task 64 (WCST64) and a digitally delivered WCST64; implementing
time restraints and complexity of choices in the WCST64; and incorporating a dual task

Manipulating Cognitive Intensity During Aerobic Exercise
xii
environment of walking and WCST64 completion, this project aims to uncover how a variety of
variables affect performance on cognitive tasks in both older and younger adults.
A total of 40 recruited participants were equally allocated into two groups according to
age (18-29: young adult; 65-75: older adult). After a collection of baseline descriptive data and
acquisition to the Wisconsin Card Sorting Task (WCST64), participants completed 7 trials of the
WCST64 under conditions with manipulated variables (time constraint, complexity, dual
tasking). A 10 Meter Walk test was collected both before and after intervention as a measure of
fatigue. Cognitive performance was measured by the number of errors in the WCST64; gait
performance was measured by changes in average gait speed and stride length as compared to
the baseline. A paired t-test was used to assess the difference between the conventional and
digital version of the WCST64; the effect of progressive load (i.e., time constraint, number of
sorting option, etc.) on WCST64 scores while stationery and walking was assessed using a
mixed-effect linear model.
There was no significant difference between the traditional and digital delivery of the
WCST64 (p=0.152), and this difference did not vary by age group (p=0.845). There was a
significant difference in errors across all four conditions while stationary and while walking
(both p<0.0001). Additionally, the conditional effect was significantly dependent on age group
(p=0.008; p=0.047 respectfully). Stride length and gait speed were both significantly different
across conditions (p=0.0003; p<0.0001 respectfully). These results support the development of
progressively loading cognitive tasks during aerobic + cognitive exercises that may improve the
specific cognitive domain of executive function (EF), particularly as a preventative intervention
for age associated cognitive decline.

1
CHAPTER I:
AIMS
Increase in human lifespan and sedentary behavior are exacerbating the prevalence of
age-associated cognitive decline worldwide, yet there is no medical treatment for this decline.
There is, however, a specific intervention that has the potential to attenuate this problem that has
not yet been optimized in humans. Promising research has identified that physical activity
induced adaptations in neuroplasticity may mediate cognitive improvements through the
facilitation of exercise-induced neurotrophic modulators such as lactate (Jakowec et al., 2016).
Neuroplasticity describes the brain’s ability to encode experiences and learn new behaviors by
reorganizing itself structurally and physiologically (Petzinger et al., 2013). Growing evidence
supports that there is an experience-dependent neuroplasticity that requires an environment that
is simultaneously (1) cognitively engaging, promoting alteration of neural synapses
(synaptogenesis), and (2) aerobically challenging, facilitating improved circulatory function in
the brain (angiogenesis) (Jakowec et al., 2016). When combining aerobically challenging
movement with cognitively challenging tasks, this dual tasking forces a split in attentional
resources making the combination more demanding than either approach alone. Commonly
during dual tasking, a personalized motor task is imposed on a general cognitive task to induce
interference and increase cognitive demand. While many recent investigations employ aerobic +
cognitive interventions to target cognitive improvements, dosage parameters for these cognitive
challenges are not well established. To optimize aerobic + cognitive exercise prescription,
researchers must know to what degree a cognitive task is challenging an individual and how to
progressively scale these challenges. As an initial step, researchers must first show that cognitive
tasks can be progressively loaded for a range of healthy individuals. Cognitive Load Theory

Manipulating Cognitive Intensity During Aerobic Exercise
2
(CLT) provides insights into defining cognitive load and theoretically manipulating it through
factors such as task format, time constraint, task complexity, and dual-tasking (Van Merriënboer
& Sweller, 2005). A limitation that we have identified in developing aerobic + cognitive exercise
prescription is that CLT is currently not incorporated in the development of such interventions
because there are no clear instructions for doing so.
To assess the potential of developing a schema for reliably loading a cognitive task used
for aerobic + cognitive exercise, we will use a digital Wisconsin Card Sorting Test 64
(WCST64). The WCST64 is a common cognitive task used to broadly assess executive function
(EF) in research and clinical practice (Miles et al., 2021). Since walking utilizes some of the
same, limited mental resources as EF, as a WCST64 becomes more difficult, the performance of
the aerobic walking task could be affected (Yogev-Seligmann et al., 2008). Using a crossover
design, blocks of younger and older adults will complete the WCST64 under varying conditions
to determine if cognitive difficulty can be progressively loaded in a systematic way. Our research
aims to use insights from CLT to define reliable cognitive paradigms that elicit increased
cognitive load. In the dual task conditions, walking will be superimposed on the WCST64
conditions using a non-motorized treadmill outfitted with load sensors to quantify gait
mechanics. We will also investigate changes in gait mechanics (speed, variability) during
different conditions as potential indirect indicators of changes in cognitive load of the EF task.
Our primary indicator of EF performance will be WCST64 score.
Aim 1: To determine the effect that delivery method (traditional card based WCST64 vs Blue
Goji digital WCST64) has on performance of a WCST64 in older and younger adults.
Hypothesis 1: Older and younger adults will have similar cognitive performance on the Blue
Goji digital WCST64 compared to the standard WSCT64 defined by WCST64 score.

Manipulating Cognitive Intensity During Aerobic Exercise
3
Aim 2: To determine the effect that different combinations of time constraints and number of
sorting options (complexity) has on performance of a WCST64 in older and younger adults.
Hypothesis 2: Older and younger adults will have decreased cognitive performance defined by
WCST64 score as trial selection time decreases and as sorting options increase on the Blue Goji
digital WCST64. Older adults will have a steeper rate of decline than younger adults.
Aim 3: To determine the effect that dual task walking (DTW) has on gait and performance on
the digital WCST64 between conditions.
Hypothesis 3: In the different DTW conditions, changes in gait speed/variability will correlate to
changes in performance on the digital WCST64 defined by WCST64 score decreases.
The proposed study will establish a foundation for systematically manipulating cognitive
load to optimize exercise prescription for aerobic + cognitive exercise that aims to promote
improved EF.

Manipulating Cognitive Intensity During Aerobic Exercise
4
CHAPTER II:
BACKGROUND & SIGNIFICANCE
Statement of the Problem
Increase in human lifespan and sedentary lifestyle influenced by technological
advancement are exacerbating the prevalence of age-associated cognitive decline worldwide
(Kohl et al., 2012; Lazarus & Harridge, 2018); yet there is no medical treatment for this decline.
There is however a specific intervention that has the potential to attenuate this problem that has
not yet been optimized in humans. It has not yet been determined how physical activity
influences cognitive outcomes, but its positive effects on this domain of health have been well
documented (Baumgart et al., 2015). It is important to understand how physical activity can be
utilized to attenuate cognitive decline because cognition is indicative of daily function and
quality of life, especially in older adults (Wollesen et al., 2020). Promising research has revealed
that physical activity induced adaptations in neuroplasticity may mediate cognitive
improvements through the facilitation of exercise-induced neurotrophic factors (Jakowec et al.,
2016). Neuroplasticity describes the brain’s ability to encode experiences and learn new
behaviors by reorganizing itself structurally and physiologically (Petzinger et al., 2013).
Growing evidence supports the idea of an experience-dependent neuroplasticity that requires an
environment that is simultaneously (1) cognitively engaging, promoting alteration of neural
synapses (synaptogenesis), and (2) aerobically challenging, facilitating improved circulatory
function in the brain (angiogenesis) which both promote neurogenesis (Jakowec et al., 2016;
Rashid et al., 2020). When combining aerobically challenging movement with cognitively
challenging tasks, this dual tasking forces a split in attentional resources making the combination
more demanding than either approach alone. These types of interventions have not been

Manipulating Cognitive Intensity During Aerobic Exercise
5
optimized in humans because they are currently investigated with exercise without sensorymotor and cognitive procedures that can be personalized (Canning et al., 2020). Specifically,
while the intensity of angiogenesis-related motor tasks can be personalized through mechanisms
such as increased speed, there is no standard procedure for personalized loading of
synaptogenesis-related cognitive activity (Jakowec et al., 2016). Currently, the way that
cognitive challenge is implemented during aerobic + cognitive exercise is not well established.
However, the Cognitive Load Theory (CLT), actually defines cognitive load and presents a
theoretical framework for how it can be manipulated (Deng et al., 2021). CLT defines cognitive
load as a multi-dimensional construct representing the load that performing a particular task
imposes on a learner's cognitive system. Theoretical task characteristics that have been identified
in CLT research to manipulate cognitive load are task format, time constraint, task complexity,
use of multimedia, pacing of instruction, and dual tasking. Although this literature exists, it
hasn’t been incorporated in aerobic + cognitive interventions. As investigations that employ
aerobic + cognitive interventions to target cognitive improvements become more common,
researchers must know to what degree a cognitive task is challenging an individual and how to
progressively scale these challenges to optimize these interventions. As initial steps,
researchers must first show that cognitive tasks can be progressively loaded during these
aerobic + cognitive interventions.
Benefits of Cognitive + Aerobic Training
Aerobic exercise and cognitive training have been effective in improving cognitive functions in a
variety of human populations that suffer from cognitive decline from stroke to schizophrenia

Manipulating Cognitive Intensity During Aerobic Exercise
6
(Jakowec et al., 2016; Nuechterlein et al., 2022; Yeh et al., 2017). In all these cases there appears
to be a synergistic effect when both aerobic and cognitive training occur concurrently.
Potential Mechanisms
Studies suggest that combining cognitively engaging activity with aerobic training
catalyzes neuroplastic adaptation in a way that neither activity evokes alone (Petzinger et al.,
2013). Chronic aerobic exercise has long been shown to improve quality of life (Goodman et al.,
2016; Mang et al., 2013). But how does increase activity lead to improved cognitive function?
Figure 1 depicts the model of neuroplasticity induced by cognitively engaging aerobic exercise
and provides some insights into mechanisms that may mediate experience-dependent
neuroplastic development catalyzed by cognitively engaging aerobic activity (El-Sayes et al.,
2019). As mentioned earlier, growing evidence supports the idea of an experience-dependent
neuroplasticity that requires an environment that is simultaneously (1) cognitively engaging,
promoting alteration of neural synapses (synaptogenesis), and (2) aerobically challenging,
facilitating improved circulatory function in the brain (angiogenesis) which both promote
neurogenesis (Jakowec et al., 2016; Rashid et al., 2020). This figure shows that molecularly, this
type of activity increases the production of brain derived neurotrophic factor (BDNF) in an
individual. BDNF has garnered attention as a mediator for synaptogenesis because it contributes
to activity-dependent changes in the brain and has an important role in brain development and
maintenance of function (Nicastri et al., 2022). In comparison to other neurotrophic growth
factors, BDNF is highly expressed in the cerebral cortex and hippocampus. Extensive research
has shown that BDNF promotes neuroplasticity, facilitating synaptic transmission, dendritic
modification, receptor trafficking, and the process of long-term potentiation (Bramham &

Manipulating Cognitive Intensity During Aerobic Exercise
7
Messaoudi, 2005). Moreover, BDNF is known to support neurogenesis and synaptic growth and
repair.
Figure 1 also depicts that vascular endothelial growth factor (VEGF) increases because of
cognitively engaging aerobic activity. In fact, studies support that VEGF increases as a result of
any acute bout of aerobic activity (El-Sayes et al., 2019). VEGF stimulates the migration and
proliferation of arterial, venous, and microvascular endothelial cells as well as angiogenesis in
vivo and in vitro (El-Sayes et al., 2019). These molecular modifications contribute directly to
functional modifications in that they promote increased cerebral blood flow.
Figure 1. Model of neuroplasticity induced by acute aerobic exercise. Model of neuroplasticity
induced by acute aerobic exercise. Acute aerobic exercise induces greater levels of peripheral
BDNF and VEGF, increases neurotransmitter concentration, and increases glucose and oxygen
metabolism. Furthermore, acute aerobic exercise increases CBF, neural activity, and receptor
activity. Increases in neural activity are likely mediated by increased CBF and glucose and
oxygen availability. Increases in receptor activity are likely mediated by increases in
neurotransmitter concentrations and BDNF. Over time, neuroplasticity induced by acute
exercise leads to changes seen due to repeated exercise. BDNF = brain-derived neurotrophic
factor; VEGF = vascular endothelial growth factor; NT = neurotransmitter; CBF = cerebellar
blood flow; O2= oxygen.
Preliminary Information
Two preliminary studies were used to inform our study data collection was executed to
inform the protocol for this study.

Manipulating Cognitive Intensity During Aerobic Exercise
8
Pilot 1
Preliminary Pilot Aim 1: To determine the effect that delivery method has on performance of a
WCST64 in young adults.
Preliminary Pilot Hypothesis 1: Young adults will have similar cognitive performance on a
digital WCST64 compared to the standard WSCT64.
Preliminary Pilot Aim 2: To determine the effect that time constraints have on performance of
a WCST64 in young adults.
Preliminary Pilot Hypothesis 2: Young adults will have decreased cognitive performance as
trial selection time decreases.
Preliminary Pilot Aim 3: To determine the effect that the number of sorting options has on
performance of a WCST64 in young adults.
Preliminary Pilot Hypothesis 3: Young adults will have decreased cognitive performance as
sorting options increase on a digital WCST64.
Preliminary Pilot Aim 4: To determine the effect that walking has on performance of a
WCST64 in young adults.
Preliminary Pilot Hypothesis 4: Young adults will have decreased cognitive performance
during walking.
Pre-Pilot Study Design: This study was conducted in the Clinical Exercise Research Center
(CERC) in the Division of Biokinesiology and Physical Therapy on the University of Southern
California Health Sciences Campus. In this cross-sectional intervention, participants were
randomized at 2 nested levels to test the effects that manipulating variables of a WSCT64 had on
performance of the task. The first level of randomization was to determine which of the research

Manipulating Cognitive Intensity During Aerobic Exercise
9
aims that each participant would test, and the second level of randomization was to determine the
protocol order for the aim. For aim 1, the order of digital vs traditional delivery method was
randomized. For aim 2, the order of no time constraint, 2 second time constraint, and 3 second
time constraint was randomized. For aim 3, the order of 3 sorting options, 4 sorting options, 5
sorting options, and 6 sorting options were randomized. For aim 4, the order of standing vs
walking was randomized.
Pilot 1 Participant Recruitment
The primary reason young adults were used for this Pilot was to support convenience
sampling of participants who are enrolled in USC Division of Biokinesiology and Physical
Therapy. For each pilot aim 6 independent participants were recruited. There is little support for
significant sex differences in EF, so gender was not stratified in recruitment (Grissom & Reyes,
2019). Interested participants reported to the CERC and were given the opportunity to ask any
questions they had about the pilot study. After inclusion was confirmed, participants were
randomized at the 2 levels.
Inclusion Criteria:
• 18–29-year-old men and women
• Able to see and hear sufficiently to participate
Exclusion Criteria:
• History of known neurological disease (e.g., Epilepsy, Multiple sclerosis, Parkinson disease,
Alzheimer’s disease), cerebral infarct (e.g., Stroke), or traumatic brain injury
• Musculoskeletal injuries interfering with the ability to walk

Manipulating Cognitive Intensity During Aerobic Exercise
10
Pilot 1 Intervention:
Pilot 1 Aim 1: Delivery Method
Participants conditions were randomized (traditional delivery method, digital delivery
method). Each condition ranged from 2-7 minutes. For the traditional delivery method,
participants completed the WCST64 using the traditional card deck in a standing position
(Instruction: This test is a little unusual because I am not allowed to tell you very much about
how to do it. You will be asked to match each of the cards in the deck (point to the repose card
deck) to one of these four key cards (point to each of the stimulus cards in succession, beginning
with the red triangle). You must always take the top card from the deck and place it below the
key card you think it matches. I cannot tell you how to match the cards, but I will tell you each
time whether you are right or wrong. If you are wrong, simply leave the card where you have
placed it and try to get the next card correct. There is no time limit on this test.) For the digital
delivery method, participants completed the digital version on the WCST 64 on the Blue Goji in
a standing position. Both conditions had 64 trials, 4 sorting options, and unlimited selection time.
Participants had a 1–2-minute break between conditions. (Instruction: This test is a little unusual
because I am not allowed to tell you very much about how to do it. You will be asked to match
each of the cards that show up at the bottom of the screen to one of the four key cards at the top
of the screen. To navigate to your choice, use the left and right arrows on the controller on the
left handrail. To select your choice, press the “A” button on the controller on the right handrail. I
cannot tell you how to match the cards, but a green check mark will appear each time you are
right and a red “X mark” will appear when you are wrong. If you are wrong, try to get the next
card correct. There is no time limit on this test.)

Manipulating Cognitive Intensity During Aerobic Exercise
11
Pilot 1 Aim 2: Selection Time
Participants conditions were randomized (no selection time limit, 2 second selection time
limit, and 3 second selection time limit). Each trial ranged from 2-7 minutes. All conditions used
the digital delivery method, and participants completed the digital version on the WCST64 on
the Blue Goji in a standing position. Each condition consisted of 64 trials using the digital
WCST64, and 4 sorting options. Participants had 3-5 minutes break between conditions.
(Instruction: This test is a little unusual because I am not allowed to tell you very much about
how to do it. You will be asked to match each of the cards that show up at the bottom of the
screen to one of the four key cards at the top of the screen. To navigate to your choice, use the
left and right arrows on the controller on the left handrail. To select your choice, press the “A”
button on the controller on the right handrail. I cannot tell you how to match the cards, but a
green check mark will appear each time you are right and a red “X mark” will appear when you
are wrong. If you are wrong, try to get the next card correct. There is a time limit for each trial on
this test.)
Pilot 1 Aim 3: Number of Sorting Options
Participants conditions were randomized (3 sorting options, 4 sorting options, 5 sorting
options, 6 sorting options). Each trial ranged from 5-15 minutes. All conditions used the digital
delivery method, and participants completed the digital version on the WCST64 on the Blue Goji
in a standing position. Each condition consisted of 64 trials using the digital WCST64 task in a
standing position, and unlimited selection time. Participants had 3-5 minutes break between
conditions. (Instruction: This test is a little unusual because I am not allowed to tell you very
much about how to do it. You will be asked to match each of the cards that show up at the

Manipulating Cognitive Intensity During Aerobic Exercise
12
bottom of the screen to one of the three, four, five or six key cards at the top of the screen. To
navigate to your choice, use the left and right arrows on the controller on the left handrail. To
select your choice, press the “A” button on the controller on the right handrail. I cannot tell you
how to match the cards, but a green check mark will appear each time you are right and a red “X
mark” will appear when you are wrong. If you are wrong, try to get the next card correct. There
is no time limit on this test.)
Pilot 1 Aim 4: Walking
Participants conditions were randomized (digital standing vs digital walking). Each
condition ranged from 3-8 minutes. For the standing digital delivery method, participants
completed the digital version of the WCST64 on the Blue Goji. For the walking delivery method,
participants completed the digital version of the WCST64 on the Blue Goji while walking at a
self-selected pace. Both conditions had 64 trials, 4 sorting options, and unlimited selection time.
Participants had 1–2-minute break between conditions. (Standing Instruction: This test is a little
unusual because I am not allowed to tell you very much about how to do it. You will be asked to
match each of the cards that show up at the bottom of the screen to one of the four key cards at
the top of the screen. To navigate to your choice, use the left and right arrows on the controller
on the left handrail. To select your choice, press the “A” button on the controller on the right
handrail. I cannot tell you how to match the cards, but a green check mark will appear each time
you are right and a red “X mark” will appear when you are wrong. If you are wrong, try to get
the next card correct. There is no time limit on this test.) (Walking Instruction: You must walk as
quickly as you can sustain for the duration of this test while holding the handrails. This test is a
little unusual because I am not allowed to tell you very much about how to do it. You will be

Manipulating Cognitive Intensity During Aerobic Exercise
13
asked to match each of the cards that show up at the bottom of the screen to one of the four key
cards at the top of the screen. To navigate to your choice, use the left and right arrows on the
controller on the left handrail. To select your choice, press the “A” button on the controller on
the right handrail. I cannot tell you how to match the cards, but a green check mark will appear
each time you are right and a red “X mark” will appear when you are wrong. If you are wrong,
try to get the next card correct. There is no time limit on this test.)
Pilot 1 Statistical Analysis/Results
Pilot 1 Aim 1: Delivery Method
Data were analyzed to investigate (1) difference between conditions and (2) learning
effect (analyzed by comparing the score based on order of trials). Data were parametric so the
paired samples t-test was used to compare the means of the two conditions in each analysis.
There was no statistical significance between condition (p=0.48), and the effect size was small
(es=0.31) in this sample (Fig 2). The power (1-β) was calculated at 0.09. The order of trials
approached significance (p= 0.08) and a large effect size (es=0.89) (Fig 3). The power (1-β) was
calculated at 0.42.
Figure 2. Means ± standard deviation (SD) of traditional WCST64 and digital Blue Goji
WCST64 *denotes significant difference between conditions at p < 0.05

Manipulating Cognitive Intensity During Aerobic Exercise
14
Figure 3. Means ± standard deviation (SD) of first trial vs second trial of the digital Blue Goji
WCST64 *denotes significant difference between conditions at p < 0.05
Pilot 1 Aim 2: Selection Time
Data were analyzed to investigate (1) difference between conditions and (2) learning
effect (analyzed by comparing the score based on order of trials). Data were parametric so the
one-way repeated measures ANOVA (within-subjects ANOVA) was used to determine whether
there were any statistically significant differences between the means of the three conditions in
each analysis. There was no statistical significance between condition (p=0.38), and the effect
size was small (es=0.38) in this sample (Fig 4). The power (1-β) was calculated at 0.15. There
was no statistical significance associated with the order that the trials were performed (p=0.36),
and the effect size was small (es=0.15) in this sample (Fig 5). The power (1-β) was calculated at
0.08.

Manipulating Cognitive Intensity During Aerobic Exercise
15
Figure 4. Means ± standard deviation (SD) of the digital Blue Goji WCST64 trials with unlimited
selection time vs 3 second selection time vs 2 second selection time *denotes significant
difference between conditions at p < 0.05
Figure 5. Means ± standard deviation (SD) of first trial vs second trial vs third trial of the digital
Blue Goji WCST64
*denotes significant difference between conditions at p < 0.05
Aim 3: Number of Sorting Options
Data were analyzed to investigate (1) difference between conditions and (2) learning
effect (analyzed by comparing the score based off order of trials). Data were parametric so the
one-way repeated measures ANOVA (within-subjects ANOVA) was used to determine whether
there were any statistically significant differences between the means of the four conditions in
each analysis. There was no statistical significance between condition (p=0.46), and the effect
size was moderate (es=0.53) in this sample (Fig 6). The power (1-β) was calculated at 0.13.
There was no statistical significance associated with the order that the trials were performed

Manipulating Cognitive Intensity During Aerobic Exercise
16
(p=0.22), and the effect size was moderate (es=0.72) in this sample (Fig 7). The power (1-β) was
calculated at 0.24.
Figure 6. Means ± standard deviation (SD) of the Blue Goji digital WCST64 trials with 3 sorting
options vs 4 sorting options vs 5 sorting options vs 6 sorting options *denotes significant
difference between conditions at p < 0.05
Figure 7. Means ± standard deviation (SD) of first trial vs second trial vs third trial vs fourth
trial of the digital Blue Goji WCST64 *denotes significant difference between conditions at p <
0.05
Aim 4: Walking
Data were analyzed to investigate (1) difference between conditions and (2) learning
effect (analyzed by comparing the score based off order of trials). Data were parametric so the
paired samples t-test was used to compares the means of the two conditions in each analysis.

Manipulating Cognitive Intensity During Aerobic Exercise
17
There was no statistical significance between condition (p=0.74), and the effect size was small
(es=0.14) in this sample (Fig 8). The power (1-β) was calculated at 0.06. There was no statistical
significance associated with the order that the trials were performed (p=0.61), and the effect size
was small (es=0.22) in this sample (Fig 9). The power (1-β) was calculated at 0.07.
Figure 8. Means ± standard deviation (SD) of walking condition vs standing condition of the
digital Blue Goji WCST64 *denotes significant difference between conditions at p < 0.05
Figure 9. Means ± standard deviation (SD) of first trial vs second trial of the digital Blue Goji
WCST64 *denotes significant difference between conditions at p < 0.05
Pilot 1 Discussion:
Results show that the pre-pilot was underpowered to draw statistical conclusions, but the
trends observed are useful in finalizing the protocol for my dissertation. As it relates to power (1-
β), 40-60 participants will be required for 1-β=0.8 informed by the data collected in our pre-pilot.

Manipulating Cognitive Intensity During Aerobic Exercise
18
The trends observed also support a strong learning effect (Fig 3, Fig 5, Fig 7, Fig 9). To address
this in the dissertation work I will incorporate acquisition trials. The trends observed in pre-pilot
aim 1 data support that there is no difference in performance on the WCST64 between traditional
administration and the Blue Goji digital administration of the task (Fig 2). Trends in pre-pilot
aim 2 support that decreased selection time for trials was associated with poorer performance on
the digital WCST64 even in the presence of the learning effect (Fig 4). This is promising for our
goal of challenging EF because it appears that independent of the amount of practice trials, the
group performed worse with a 2 second time constraint. Similarly, the trends in pre-pilot aim 3
support that even in the presence of the learning effect, the group performed best in the condition
with the least sorting options and performed worst in the condition with the most sorting options
(Fig 6). There are no clear trends to report from pre-pilot aim 4, however successful execution of
this aim by all participants supports the feasibility of our dual tasking aims for the dissertation
pre-pilot study. An additional insight that was learned from this pre-piloting is the need to adjust
the rules for administering the digital WCST64. When administered traditionally, the participants
must match their trial card to one of four key cards by color first for 10 consecutive trials, then
form for 10 consecutive trials, and then number for 10 consecutive trials. If participants complete
these successfully, this order repeats. During pre-piloting, we observed that after 2 attempts at
the task, participants learned the order and the task moved from assessing EF to memory.
Traditionally, the WCST64 is not meant to be repeated consecutively so this is not typically a
problem; however, for my dissertation research participants will be required to complete the
digital WCST64 under multiple conditions. To ensure that the task continues to measure EF, the
order of the sorting rule will be randomized between conditions.
Pilot 2

Manipulating Cognitive Intensity During Aerobic Exercise
19
Although underpowered, these preliminary results showed useful trends helpful in
determining the methods for the final dissertation protocol and approach.
Pilot 2 Aim 1: To determine the effect that delivery method (traditional card based WCST64 vs
Blue Goji digital WCST64) has on performance of a WCST64 in older and younger adults.
Pilot 2 Hypothesis 1: Older and younger adults will have similar cognitive performance on the
Blue Goji digital WCST64 compared to the standard WSCT64 defined by WCST64 score.
Pilot 2 Aim 2: To determine the effect that different combinations of time constraints and
number of sorting options (complexity) has on performance of a WCST64 in older and younger
adults.
Pilot 2 Hypothesis 2: Older and younger adults will have decreased cognitive performance
defined by WCST64 score as trial selection time decreases and as sorting options increase on the
Blue Goji digital WCST64. Older adults will have a steeper rate of decline than younger adults.
Pilot 2 Aim 3: To determine the effect that DTW has on gait and performance on the digital
WCST64 between conditions.
Pilot 2 Hypothesis 3: In the different DTW conditions, changes in gait speed/variability will
correlate to changes in performance on the digital WCST64 defined by WCST64 score
decreases.
The trends observed in this preliminary pilot aim 1 data analysis supported that there was
no difference in performance on the WCST64 between traditional administration and the Blue
Goji digital administration of the task. Trends in pre-pilot aim 2 support that decreased selection
time for trials was associated with poorer performance on the digital WCST64 even in the
presence of the learning effect. This was promising for our goal of challenging EF. Similarly, the
trends in this preliminary pilot aim 3 supported that even in the presence of the learning effect,

Manipulating Cognitive Intensity During Aerobic Exercise
20
the group performed best in the condition with the least sorting options and performed worst in
the condition with the most sorting options. An additional insight that was learned from this
piloting is the need to adjust the rules for administering the digital WCST64. When administered
traditionally, the participants must match their trial card to one of four key cards by color first for
10 consecutive trials, then form for 10 consecutive trials, and then number for 10 consecutive
trials. If participants complete these successfully, this order repeats. During piloting, we
observed that after 2 attempts at the task, participants learned the order and the task moved from
assessing EF to memory. Traditionally, the WCST64 is not meant to be repeated consecutively
so this is not typically a problem; however, for my dissertation research participants will be
required to complete the digital WCST64 under multiple conditions. To ensure that the task
continues to measure EF, the order of the sorting rule will be randomized between conditions.
Our preliminary pilot study followed the protocol outlined in the dissertation approach section
using 1 older adult, and 1 younger adult.
Pilot 2 Preliminary Results
Table 1. Participant Descriptive Data
Younger Adult Older Adults
N 1 1
Age (years) 27 72
BMI 18.8 (below average) 28.4 (above average)
Aerobic Capacity (VO2
Max)
51.5 (above average) 30.0 (above average)
Traditional WCST64 Score 58 (above average) 29 (mild impairment)
Table 2. Traditional vs Digital WCST64 Score by Group
Younger Adult Older Adults
Traditional WCST64 Score 57 (above average) 40 (average)
Digital WCST64 Score 58 (above average 39 (below average)

Manipulating Cognitive Intensity During Aerobic Exercise
21
Figure 10. Visualization of WCST64 performance assessed by score using the traditional vs
digital delivery method by age group
Table 3. Digital WCST64 Scores for Each Condition by Group
Condition Younger Adult Older Adults
¥ time, 4 sorting, single
task
58 (above average) 39 (below average)
baseline condition baseline condition
¥ time, 6 sorting, single
task
57 (above average) 24 (mild-moderate
impairment)
-1.7% D from baseline -47.6% D from baseline
2 sec, 4 sorting, single task 53 (average) 26 (mild-moderate
impairment)
-9.0% D from baseline -40.0% from baseline
2 sec, 6 sorting, single task 49 (below average) 6 (severe impairment)
-16.8% D from baseline -146.7% D from baseline
¥ time, 4 sorting, dual task 52 (average) 35 (below average)
-10.9% D from baseline -10.8% D from baseline
¥ time, 6 sorting, dual task 56 (above average) 40 (average)
-3.5% D from baseline 2.5% D from baseline
2 sec, 4 sorting, dual task 53 (average) 12 (moderate-severe
impairment)
-9.0% D from baseline -105.9% D from baseline
2 sec, 6 sorting, dual task 42 (mild impairment) 17 (moderate impairment)
-32.0% D from baseline -78.6% D from baseline
57
58
40 39
0
20
40
60
80
WCST64 Score
Traditional Digital
Delivery Method
Traditional vs Digital WCST64 Score by Group
Younger Adult Older Adult

Manipulating Cognitive Intensity During Aerobic Exercise
22
Figure 11. Visualization of WCST64 performance assessed by score on all interventions by age
group and looking at time constraint as a main effect
0
10
20
30
40
50
60
70
2 sec unlimited
WCST64 Score
Time Pressure
Digital WCST64 Scores for Each Condition by Group: Time Constraint as Main
Effect
Younger Adult: single task, 4 options Older Adult: single task, 4 options
Younger Adult: dual task, 4 options Older Adult: dual task, 4 options
Younger Adult: single task, 6 options Older Adult: single task, 6 options
Younger Adult: dual task, 6 options Older Adult: dual task, 6 options

Manipulating Cognitive Intensity During Aerobic Exercise
23
Figure 12. Visualization of WCST64 performance assessed by score on all interventions by age
group and looking at complexity as a main effect
0
10
20
30
40
50
60
70
4 options 6 options
WCST64 Score
Complexity
Digital WCST64 Scores for Each Condition by Group: Complexity as Main
Effect
Younger Adult: single task, unlimited seconds Older Adult: single task, unlimited seconds
Younger Adult: dual task, unlimited seconds Older Adult: dual task, unlimited seconds
Younger Adult: single task, 2 seconds Older Adult: single task, 2 seconds
Younger Adult: dual task, 2 seconds Older Adult: dual task, 2 seconds

Manipulating Cognitive Intensity During Aerobic Exercise
24
Figure 13. Visualization of WCST64 performance assessed by score on all interventions by age
group and looking at dual tasking as a main effect
Table 4. Post-Test Fatigue Data Assessed by 10 Meter Walk Test
Younger Adult Older Adults
10 Meter Walk Test Fatigue 0.7% D from baseline 0.3% D from baseline
0
10
20
30
40
50
60
70
Single Task Dual Task
WCST64 Score
Dual-Tasking
Digital WCST64 Scores for Each Condition by Group: Dual Tasking as Main
Effect
Younger Adult: 4 options, unlimited seconds Older Adult: 4 options, unlimited seconds
Younger Adult: 6 options, unlimited seconds Older Adult: 6 options, unlimited seconds
Younger Adult: 4 options, 2 seconds Older Adult: 4 options, 2 seconds
Younger Adult: 6 options, 2 seconds Older Adult: 6 options, 2 seconds

Manipulating Cognitive Intensity During Aerobic Exercise
25
Pilot 2 Study Discussion
This preliminary data shows promise that this study will provide statistical data to answer
the aims of this research. Table 2 and Figure 10 support that participants have similar
performance on the WCST64 on both the traditional and digital delivery. This supports our
hypothesis for Aim 1. This finding helps us justify relying on the digital WCST64 for the
remainder of our aims. Table 3 and Figures 11, 12, and 13 provide results that address Aim 2.
Figure 11 support our hypothesis that as time decreases for trials, performance on the WCST64
declines. It also supports that this rate of decline is steeper in the older adult pilot participant
compared to the younger adult. Figure 12 and 13, show less unanimous results to support the
effects of dual-tasking and complexity on WCST64 performance, so more participants will be
needed before speculating the role of these variables as main effectors. For aim 3, gait data was
collected at 50 Hz using 4 load sensors housed in the Blue Goji treadmill during all dual task
digital WCST64 interventions. During acquisition, all participants were given a target speed to
maintain throughout all dual task interventions. To determine baseline stride variability
participants walked at this speed with speedometer feedback for 3 minutes. Due to slow Wi-Fi
upload speeds this data is not available to be presented in this proposal. To solve this issue for
dissertation data collection, the Blue Goji treadmill will be connected to the internet via ethernet.
Stride variability will be reported in 2 ways: (1) standard deviation from target gait speed and (2)
percentage of trial that is greater than 1 standard deviation away from the target gait speed. Once
we have this data, we hypothesize that we will see correlations between changes in gait and
changes in WCST64 performance. Once we have collected data with all 40 participants, we will
work with our biostatistician for power analysis and sample size calculations for future studies.

Manipulating Cognitive Intensity During Aerobic Exercise
26
Pilot 2 Preliminary Results Interpretation
These preliminary results provide two important findings. First it justifies our use of the
digital WCST64 for dissertation Aim 2 and 3. Second, it provides the first empirical clinical
evidence of executive function intensity being systematically manipulated in aerobic + cognitive
exercise interventions. While we have not yet provided gait data for these preliminary results, we
have imposed varying levels of intensity on the WCST64 in our interventions. These varying
levels of WCST64 interventions provide multiple performance changes to correlate to gait
changes for Aim 3.

Manipulating Cognitive Intensity During Aerobic Exercise
27
CHAPTER III:
LITERATURE REVIEW
PART 1: NORMAL COGNITIVE AGING
Introduction
Biological aging can be broken into two categories: (1) primary aging which refers to the
inevitable, intrinsic, gradual processes that affect all members of a species over time such as the
decrease in rate of cellular division throughout life and (2) secondary aging which refers to
processes experienced by most, but not all, members of a species influenced by the environment
and individual behavior such as disease (Erber & EBSCOhost, 2020). This distinction in aging
categories has goaded researchers to explore the idea that primary aging is not necessarily
accompanied by decline in function, but rather makes individuals more susceptible to secondary
aging factors. Over the past decades, researchers have used this differentiation to define
successful aging (Yaffe et al., 2009). This term implies that later life is no longer characterized
by illness and dependence but rather as a time to maintain health (Kim & Park, 2017). While
early attempts to define successful aging focused on characteristics of physical well-being such
as physical disability, it was quickly identified that normal cognitive aging (NCA) is as
paramount to the definition as physical well-being and can also be categorized as either primary
or secondary (Depp & Jeste, 2006; Yaffe et al., 2009). Specifically, there are three components
of successful aging that are accepted universally: (1) avoiding disease or disability, (2)
maintaining high cognitive and physical function, and (3) prolonging active engagement in life
(Rowe & Kahn, 1987). Theoretically, primary cognitive aging in the absence of disease related
secondary cognitive aging is the definition of NCA; however, because the vast scientific
literature on cognition and aging concentrates on disease, there is not a clear clinical definition of
NCA (Jagust, 2013; Nyberg, 2019). It is imperative to give attention to any gaps in knowledge

Manipulating Cognitive Intensity During Aerobic Exercise
28
associated with successful aging because the population of older adults is increasing drastically.
The global population aged 60 years or over numbered 962 million in 2017, more than twice as
large as in 1980 when there were 382 million older persons worldwide. The number of older
persons is expected to double again by 2050, when it is projected to reach nearly 2.1 billion,
accounting for almost 25% of the world’s population (Division, 2019). The purpose of the
present literature review is to use current literature to practically define NCA.
Cognition and Aging
Cognition refers to the mental functions involved in attention, thinking, understanding,
learning, remembering, problem solving, and decision-making that are executed by the brain
(Blazer, 2015). As mammals age, the brain changes physically in structure as well as
functionally in its ability to execute mental functions (Jagust, 2013). Across society NCA has
become synonymous with cognitive decline; however, a growing body of evidence shows that
the full spectrum of cognitive aging encompasses both gains and losses (Turner, 2019). An
accurate representation of NCA must consider the dual nature of fluid and crystallized
intellectual ability through the lifespan. Fluid intelligence reflects cognition necessary for
assessing aspects of the environment, inhibiting distractions, and flexibly allocating processing
resources to sustain goal-directed behaviors while crystallized intelligence indexes the collective
store of semantic knowledge about ourselves and the world that is accumulated over the life
course (Turner, 2019). As depicted in Figure 14, while fluid cognition decreases with aging
across populations, crystalized intelligences increases with age (Turner, 2019).

Manipulating Cognitive Intensity During Aerobic Exercise
29
Figure 14. The shifting architecture of cognition across the adult life span. Cool colors represent
age-related changes on tasks that have greater reliance on cognitive control and speeded
processing. Warm colors represent age-related changes on tasks that have a greater reliance on
semantics or prior-knowledge representations. As can be seen, the architecture of cognition
undergoes a striking shift across the adult life span.
Fluid and crystalized cognition are performance measures correlated to age, but do not
explain the structural and functional changes of the aging brain. Structurally, changes in gray
matter, white matter, and ventricular volumes are a hallmark of normal brain aging (Spreng
2019). Changes to the brain’s white matter, axonal projections supporting communication among
spatially distributed networks, are also a prominent feature of brain aging and are a strong
predictor of age-related cognitive decline (Spreng 2019). Functionally, aging is associated with a
pattern of functional brain change commonly referred to as neural dedifferentiation, or an
inability to recruit specialized neural circuits associated with discreet processing operations
(Spreng 2019). The causes of the normal changes in cognition, neural function, and brain
structure experienced by older people are debated because of the difficulty of separating age and

Manipulating Cognitive Intensity During Aerobic Exercise
30
disease; however, there are two identified types of age-related morphological alterations distinct
from neurodegenerative disease that occur in animals and humans. One is loss of synaptic
density, particularly in prefrontal cortex (PFC) (Morrison & Baxter, 2012). The other is neuronal
loss in the dentate gyrus (DG), which is a part of the hippocampus thought to contribute to the
formation of new episodic memories and the exploration of novel environments. Furthermore,
researches have begun to characterize three common age-related diseases as late stage
maladaptive outcomes to structural brain changes associated with normal aging: (1) Alzheimer’s
disease (AD), cerebrovascular disease (CVD), and Parkinson’s disease (PD) (Jagust, 2013).
Older adults that exhibit any or multiple of these structural deficits in the absence of
neurodegenerative decline are case definers of NCA. Studying these asymptomatic populations
could be the key to identifying neural adaptations that mediate their cognitive protections as well
as identifying the risk factors for maladaptive outcomes.
Prefrontal Cortex Loss of Synaptic Density
Functionally, one of the most persistent patterns of age-related brain change observed
during goal-directed tasks is enhanced bilateral recruitment of lateral prefrontal cortices (Spreng
2019). Lateral prefrontal cortices, and connections with posterior and subcortical brain regions,
are critical for implementing cognitive-control processes. Increased engagement of prefrontal
cortices may reflect greater control demands in older versus younger adults and suggests that
with age, structural and functional brain changes result in noisier inputs for cognitive processing
operations. Studies of rhesus monkeys identified that normal shrinkage with age in the
dorsolateral PFC can largely be accounted for by the loss of thin dendritic spines at
glutamatergic axospinous synapses and extensive loss of myelinated fibers (Morrison & Baxter,

Manipulating Cognitive Intensity During Aerobic Exercise
31
2012). There are three types of dendritic spines: (1) highly plastic thin spines known as ‘learning
spines,’ (2) stubby spines which have roles currently unknown, (3) and mushroom spines known
as stable ‘memory spines.’ Synaptic enhancement leads to an enlargement of thin spines into
mushroom spines. In rhesus monkeys, the mean size of the thin spine heads correlates with the
capacity to learn recognition memory tasks with a Pearson’s correlation(r) value of 0.97, the
strongest correlation ever reported between a morphological measurement and cognitive
performance (Morrison & Baxter, 2012). However, in humans, as well as monkeys, nearly half
of the thin spines are lost with aging, whereas there is no loss of mushroom spines with age
(Dumitriu et al., 2010). Consequently, the loss of these thin spines presents an increased
difficulty in learning and fluid cognition late in life, and the maintenance of mushroom spines
dictates the preservation of crystalized intelligence.
Neuronal Loss in the Dentate Gyrus
Axonal boutons are small swellings found at the terminal ends of axons. They are
typically the sites where synapses are formed between neurons and where neurotransmitters are
stored for neural communication (Hara et al., 2011). As observed in rhesus monkeys, while the
density and size of axonal boutons within the DG are similar across age, the proportion of bouton
type is shifted with age. Boutons with no apparent synaptic contacts (NSBs) increase with age
while multiple-synapse bouton (MSBs) decrease with age. The proportion of NSBs correlated
with poorer outcomes in recognition memory tasks, while higher MSBs was also correlated
success in recognition memory tasks. Together, these findings suggest that while NSBs interfere
with learning, MSBs in the DG may facilitate the acquisition of novel tasks. These age-related
changes in the hippocampus generalize to the human neuroanatomy. In this interpretation, aging

Manipulating Cognitive Intensity During Aerobic Exercise
32
is associated with the degradation of specialized hippocampal neural circuits, resulting in less
efficient neural processing.
Alzheimer’s Disease
AD is the most common cause of dementia in older adults and is structurally defined as
plaques and neurofibrillary tangles revealed on histological examination of the brain (Jagust,
2013). The plaques are composed of the aggregated beta-amyloid protein surrounded by
dystrophic and degenerating neurites while the neurofibrillary tangles are composed of the
microtubule-associated protein tau, which is hyperphosphorylated and aggregated as paired
helical filaments that disrupt synaptic communication between neurons. Up to 40% of adults in
their eighth to ninth decades live cognitively normal lives in the presence of these structural
deficits (Jagust, 2013).
Cerebrovascular Disease
Like all other vessels, cerebral vessels permit the diffusion of gas, form a physical
barrier, and regulate blood flow (Yang et al., 2017). Accounting for only 2% of total body
weight, the cerebral circulation carries up to 20% of cardiac output, and consumes up to 25% of
total oxygen and glucose indicating that the brain has major sensitivities to changes in cerebral
blood flow (Yang et al., 2017). Cerebral vessels play an active role in selecting beneficial
materials and facilitating their entry into the brain while simultaneously blocking the passage of
detrimental materials. Cerebral vessels also play roles in removing neurotoxic molecules like
amyloid plaques from the interstitial fluid. Thus, cerebrovascular disease contributes to the

Manipulating Cognitive Intensity During Aerobic Exercise
33
cognitive decline associated with NCA. 20%–30% of older people have asymptomatic cerebral
vascular disease (Jagust, 2013).
Parkinson’s Disease
PD is a disorder of the motor system that manifests as slowing of motion, tremor, rigidity,
and gait and postural instability (Jagust, 2013). Structurally, PD is characterized as the loss of
dopaminergic neurons in the pars compacta of the substantia nigra within the striatum of the
hippocampal circuitry. These neurons, which project to the striatum as the nigrostriatal pathway,
also contain Lewy bodies, an abnormally aggregated form of the protein alpha-synuclein (Jagust,
2013). The dopaminergic deficits of PD provide a useful model for understanding the
neurochemical changes that underlie cognitive aging because nigral dopaminergic loss is a
feature of aging itself. Studies of postmortem tissue have revealed loss of nigral dopaminergic
neurons and at a rate of 5%–8% per decade in asymptomatic PD adult (Jagust, 2013). Reduced
dopamine signaling within striatal-hippocampal circuitry is associated with age-related
reductions in exploratory or novelty-seeking behaviors. This suggests that as striatal dopamine
functioning declines with age, functionally, impeding cognitive processes associated with
exploration (Spreng 2019). Thus, a prominent feature of brain aging is reductions in the structure
and function of the hippocampus.
Analysis of Specific Cognitive Domain Changes with Age
When identifying cognitive decline, the Mini-mental state examination (MMSE) is the
current gold standard test, including measures of orientation, attention, memory, language and
visual-spatial skills, to determine global cognitive functioning among the elderly (Palumbo et al.,
2020). However, MMSE is poor at identifying early-stage cognitive decline, and mild cognitive

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impairment. For example, a patient with severe amnesia may score in the normal range on the
MMSE. Other worrying features are the lack of components sensitive to specific domains of
cognition (Devenney & Hodges, 2017). Furthermore, the test is not sensitive to non-pathological
changes that are characteristic of NCA. Thus, for the purpose of this review, cognitive
functioning in relation to NCA has been presented by specific domains rather than globally. A
key concept to express is that the following cognitive changes are not classified as pathological,
but as changes observed in healthy older adults throughout literature.
Sensation and Perception
Sensation is the first step of cognition, the primary source of our knowledge about the
world and its properties and refers to the ability of a person to detect a stimulus that occurs in
one of the five sensory modalities. Tests of intactness of visual, auditory, tactile, gustatory, and
olfactory senses fall into this area. The ability to identify a meaningful stimulus falls under the
domain of perception, regardless of sensory modality (Harvey, 2019). Sounds, colors, and odors
are mental constructions created by sensory processing in the cerebral cortex (Damasceno,
2020). Thus age-related changes in gray matter, white matter, and ventricular volumes of the
cerebral cortex have significant effects on sensation and perception. Age-related decline in
peripheral sensory systems has been well established in the literature. Reductions in visual
function, including in visual acuity, field of view, and contrast sensitivity, are universal in older
individuals. Hearing is also well known to decline with age, with hearing loss a widespread
phenomenon among individuals aged 80 and above (Gadkaree et al., 2016).

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Motor skills
These include several different basic elements of motor activity such as fine motor
abilities, including manual dexterity and motor speed, as well as reaction time, and more global
skills such as balance (Harvey, 2019). Finger tapping is an example of the several structured
assessments of motor abilities. As these tasks have minimal cognitive demands, they are helpful
for identification of basic motor skills problems (Harvey, 2019). Motor performance appears to
increase from childhood (7–9) to young adulthood (19–25) and decrease from young adulthood
(19–25) to old age (66–80) with older adults performing similarly to the children (Harvey, 2019).
Brain areas associated with motor skills include the cerebellum which is known for its role motor
control and motor learning as well as areas in the cerebral cortex that experience structural
atrophy with age (Zwicker et al., 2011). The low white matter volume in the PCF in both
children and the oldest adults create a case for decreased processing speed as an explanation for
the similarity in motor skill performance in both age groups (Leversen et al., 2012).
Attention and Concentration
The ability to sustain attention to a task over seconds to minutes is a cognitive function in
daily functioning. Attention and concentration are a multifaceted construct and is generally
divided into two global subdomains: selective attention and sustained attention. Concentration
generally falls under sustained attention. Middle-aged adults have the greatest capacity to remain
attentive. One explanation for sustained-attention ability peaking in the 40s is that attention is
highly trainable, and practice focusing attention throughout adulthood may further hone this
skill. Also, attention can be profoundly influenced by memory. Something as simple as having
previously viewed a picture can inform where visual attention will be directed; thus, age-related

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experience can amplify attention. There is compelling evidence that hippocampal memory can
guide attention because the hippocampal memory system rapidly encodes episodic memories
(Goldfarb et al., 2016). Sustained attention peaking in one’s 40s is also consistent with recent
studies of white matter and PFC integrity across the life span. One recent study has shown
asymmetrical maturation and degeneration processes in frontal white matter tract integrity across
the life span, which qualitatively match the pattern observed in our attentional ability
(Fortenbaugh et al., 2015). Thus, normal age-related deficits in the hippocampus and the cerebral
cortex affect attentional cognition.
Memory
Having multiple subdomains, memory is the most multifaceted cognitive domain.
Working memory is the ability to hold information in consciousness for adaptive use (Harvey,
2019). This can include information from all sensory modalities and includes verbal and
nonverbal information. Further, working memory is conceptualized to include two separable
components: maintenance of information and manipulation of information (Harvey, 2019).
Episodic memory interacts with working memory storage processes to encode, maintain, and
retrieve information into and out of longer term storage (Harvey, 2019). Recollection of daily
experiences such as what one ate for dinner the night before is episodic memory. Procedural
memory refers to motor actions or skills. Procedural memory can be dissociated from episodic
memory, in that individuals with amnesia who cannot recall essentially any verbal information
can learn and retain procedural skills. Semantic memory refers to the process of long-term
storage of verbal information, referred to as long-term memory. Prospective memory is the
ability to remember to perform tasks in the future. Prospective memory operates in two different

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formats: event-based and time-based. Event-based prospective memories consist of responses
that are triggered by a stimulus. Time-based procedural memories are triggered by specific times
(Harvey, 2019). NCA is associated with an overall decline in memory, but especially working
memory. The hippocampus plays a central role in memory processes, thus age-related
hippocampal changes attribute to these declines (Nyberg, 2019). However, memory performance
for all subdomains and especially working verbal memory are thought to benefit from lifelong
learning and can actually increase with age (Klencklen et al., 2017).
Executive functioning
Executive function is commonly referred to as reasoning or problem solving (Harvey,
2019). Executive function requires cognitive flexibility, in that problem solving, particularly of
novel tasks, requires consideration of new strategies and rapid rejection of failed efforts (Harvey,
2019). Executive function abilities depend on efficient coordination of activity across all
cognitive domains, making it vulnerable to age reductions in structural and functional brain
connectivity associated with NCA. Executive function processes are strongly associated with the
PFC and cerebellar regions which both decline in volume with NCA (Diamond, 2013; Ramanoël
et al., 2018).
Processing speed
Processing speed refers to the rate people perform perceptual, motor, and decisionmaking tasks and is related to the ability to perform high order cognitive tasks (Ebaid et al.,
2017; Eckert, 2010). A hallmark of NCA is slowed processing speed. Some study findings
provide evidence that in NCA, there are no age-related impairments when processing speed is

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adequately taken into account (Pettigrew, 2014). Such a finding suggests that age-related
declines are mediated by declines in processing speed, rather than other cognitive abilities. Agerelated changes in processing speed have been attributed to structural declines in PFC. In
addition to the PFC decline, age-related declines in cerebellar gray matter volume have been
reported (Eckert, 2010).
Language/verbal skills
Language skills include the ability to understand language, access semantic memory,
identify objects with a name, and to respond to verbal instructions with behavioral acts (Harvey,
2019). Language is an aspect of cognition that appears to be mostly resistant to age-related
decline (Mather, 2010). This is because there are multiple functional redundancies in the brain
that mediate language (Friederici, 2011). However, speaking, reading, and comprehending a
language are complex processes that involve other cognitive domains, such as attention, semantic
memory, working memory. So, it is common to find subtle deficits in language processing as
speaking requires rapid retrieval of appropriate words.
Individual Differences in NCA
Sex
Significant sex differences in specific cognitive abilities have been reported from
childhood through adulthood for decades (McCarrey et al., 2016). A recent study was used to
investigate sex differences in baseline levels of performance and rates of change in mental status
and domain-specific cognitive abilities in a large sample of well-characterized older adults
without cognitive impairment (McCarrey et al., 2016). While women outperformed men on tests

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of verbal learning, memory and fluent language production, there were no significant sex
differences in longitudinal rates of change. Women also showed higher average baseline
performance in tests of global cognition and significantly slower cognitive decline than men over
time on these tests. While men showed generally higher performance in visuospatial cognition at
baseline, men showed steeper rates of age-related cognitive decline longitudinally. In general,
there appears to be less rapid decline in women compared to men regarding NCA. Potential
explanations for this include biological contributions to cognitive sex differences that have been
documented in animal models and in humans. Studies have reported a female advantage in
proportion of gray matter volumes, white matter volumes, cerebrospinal fluid volumes, and in
corpus callosum morphology. Further, males have lower cortical thickness, exhibit greater agerelated reductions in frontal and temporal brain volumes and experience age-related cortical
thinning at a greater rate than females (McCarrey et al., 2016). Taken together, women appear to
be less vulnerable to the structural and functional brain changes associated with NCA (McCarrey
et al., 2016).
Race
The most in-depth knowledge we have on race and NCA involves cognitive decline
compared in Non-Hispanic White, Black, and Hispanic race groups. Non-Hispanic Whites have
higher baseline scores across cognitive domains such as episodic memory, semantic memory,
executive functioning, working memory, perceptual speed, vocabulary, and visuospatial function
compared to both Black (approx. 0.5 SD higher on average) and Hispanics (approx. 1 SD higher
on average) (Gross et al., 2015; Zahodne et al., 2017). In contrast, racial/ethnic differences in
rate of change in cognition are small (Gross et al., 2015). Thus, there appears to be no significant

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relationship between race and differences in NCA. The disparities between cognitive baselines
seem to be resultant of inequities in other factors such as education, wealth, and perceived
discrimination perpetuated by systemic racism (Adnan et al., 2019).
Conclusion
The goal of the current literature review was to define NCA. NCA appears to be losses
and gains across cognitive domains that result from structural and functional neuroanatomical
changes associated with aging. A key feature of NCA is slowed processing, but intact cognition.
Further, men have a steeper decline in cognitive function compared to women. This review
supports that no anatomical morphology is universally indicative of pathology, and that studying
the mechanisms that seemingly protect older adults that age “successfully” in the presence of
neuroanatomical decline may provide insights that can inform how we attenuate cognitive
decline societally. The goal of my following literature review is to explore the protective
adaptations and risk factors of age-related cognitive decline.

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PART 2: EXERCISE EFFECT ON COGNITIVE AGING
Introduction
In 2014, the World Dementia Council in partnership with the Alzheimer’s Association
summarized the strongest retrospective body of evidence for modifiable risk factors for cognitive
decline and concluded that physical activity (PA) and years of formal education were both
structurally and functionally neuroprotective in healthy older adults and may even delay the
onset, or reduce the rate of decline (Baumgart et al., 2015; Voss et al., 2010). The most
consistent findings across the body of literature were (1) inactive, but otherwise healthy, older
adults who begin a regular, moderate to vigorous metabolic (cardiovascular, resistance training)
exercise program experience improve cognitive function and (2) people with more years of
formal education measured by grade level or greater literacy have a lower risk for cognitive
decline than those with fewer years of formal education (Baumgart et al., 2015). There is a
substantial body of evidence demonstrating that regular PA can reduce the risk of the various
medical conditions, such as cardiovascular disease, diabetes mellitus, and cerebrovascular
disease which have all been identified as important risk factors for cognitive decline
(Lautenschlager et al., 2014). With these modifiable risk factors being so pervasive across the
literature, it is valuable to understand the mechanisms that PA employs to attenuate cognitive
decline associated with NCA.
The terms physical activity, exercise, and fitness are often used interchangeably but
represent distinct constructs (Hayes et al., 2013). PA has been defined as “any bodily movement
produced by skeletal muscle that results in energy expenditure” (Hayes et al., 2013). Exercise
shares characteristics of physical activity, but it is considered a distinct sub-category in that it is

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“planned, structured, repetitive and purposive in the sense that improvement or maintenance of
one or more components of physical fitness is an objective” (Hayes et al., 2013). By contrast to
both, physical fitness is “a set of attributes that people have or achieve daily tasks with vigor and
alertness, without undue fatigue and with ample energy to enjoy leisure time pursuits and to meet
unforeseen emergencies” (Hayes et al., 2013).
Pre-Frontal Cortex Loss of Synaptic Density: Exercise
Moderate aerobic exercise has been shown to enhance performance on cognitive tasks
that require the prefrontal cortex (Brockett et al., 2015). An effect of higher physical fitness on
larger brain volume in the frontal cortex has been found in many studies with older adults but not
in younger people (Voelcker-Rehage & Niemann, 2013). In a cross-sectional examination of 55
older adults, Colcombe et al. (2003) found that age-related losses in gray and white matter
tended to be greatest in the frontal, pre-frontal, and temporal regions (Kramer et al., 2003)
disrupting communication among spatially distributed networks and causing neural
dedifferentiation (Turner, 2019). In this study, there was a significant reduction of declines in
these areas as a function of cardiovascular fitness. Older adults who had better cardiovascular
fitness tended to lose less tissue in frontal, pre-frontal, and temporal regions as a function of age
(Kramer et al., 2003). Similarly, aerobically active older adults, much like young adults, tended
to show less neural dedifferentiation during cognitive tasks (Kramer et al., 2003). Other research
has since corroborated these findings, reporting that higher physical fitness is associated with
larger brain volume in the frontal cortex of older adults (Voelcker-Rehage & Niemann, 2013)

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Neuronal Loss in the Dentate Gyrus: Exercise
Regions of the hippocampus are especially vulnerable to age-related functional changes
which is why metabolic training might particularly affect brain region. Aerobic exercise is
known to increase the density of dendritic spines and numbers of synapses in the hippocampus
(Brockett et al., 2015). In line with this hypothesis, Pereira et al. found that after a 3-month
cardiovascular training with middle-aged adults, cerebral blood volume in the dentate gyrus of
the hippocampus was enhanced and coupled with improved VO2 max, suggesting better
vascularization of this tissue (Voelcker-Rehage & Niemann, 2013).
Alzheimer’s Disease: Exercise
Defining characteristics of AD are plaques and neurofibrillary tangles revealed upon
postmortem histological examination of the brain. Neurofibrillary tangles are insoluble
hyperphosphorylated tau proteins that are deposited intracellularly and cause neuroinflammation
(Rashid et al., 2020). Extracellular amyloid plaques are also found to be associated with
neuroinflammation. Animal studies of aging and AD have demonstrated that physical activity
has a positive impact on the brain, including reductions in levels of amyloid plaques, and
neurofibrillary tangles allowing new nerve cells in the hippocampus supporting cognitive
improvements such as learning and memory (Hayes et al., 2013).

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Cerebrovascular Disease: Exercise
NCA disrupts cerebrovascular plasticity and leads to cerebral hypoperfusion, which
accelerates age related cognitive decline (Nishijima et al., 2016). Regular exercise has been
demonstrated to increase angiogenesis and ameliorate an age-related decline in cerebral blood
flow that contribute to these declines (Voss et al., 2010). Increased angiogenesis is innately tied
to neuronal protection because insufficient blood supply causes neuronal dysfunction (Nishijima
et al., 2016). Conversely, it is plausible to say that improved neuronal plasticity should be
accompanied with improved cerebrovascular plasticity because neuronal activity triggers uptake
of circulating insulin-like growth factor 1 (IGF-1), a hormone that promotes tissue growth, into
the brain (Nishijima et al., 2016).
Parkinson’s Disease
PD is characterized by a progressive and selective loss of dopaminergic neurons in the
substantia nigra within the striatum of the hippocampal circuitry. Although the etiology of PD
remains unclear, neuroinflammation has been implicated in the development of PD (Wu et al.,
2011). The most well-characterized plastic change induced by regular exercise is actually
hippocampal neurogenesis, which plays a significant role in hippocampus dependent learning,
memory, and antidepressant action (Nishijima et al., 2016). In an investigation where mice were
injected with an intraperitoneal lipopolysaccharide toxin to induce reduction of dopaminergic
neurons, a running exercise intervention was introduced (Wu et al., 2011). Four weeks of
running exercises effectively protected the mice against dopaminergic neuron loss by restoring
striatal dopamine level and restoring normal performance in motor coordination tasks. These
researchers found that the protections were not due to modulation of the injected toxins, because

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the levels of proinflammatory cytokines were not different between the intervention and control
groups post intervention. The running exercise instead induced upregulated signaling pathways
for brain-derived neurotrophic factor (BDNF), a neurotrophic protein that promotes the survival
of neurons by playing a role in the growth, differentiation, and maintenance of these cells (Wu et
al., 2011).
Sensation and Perception
Sensation is the first step of cognition, the primary source of our knowledge about the
world and its properties and refers to the ability of a person to detect a stimulus that occurs in
one of the five sensory modalities. Tests of intactness of visual, auditory, tactile, gustatory, and
olfactory senses fall into this area. The ability to identify a meaningful stimulus falls under the
domain of perception, regardless of sensory modality (Harvey, 2019). While there is literature
describing populations changes with aging in sensation and perception of sensation, there are no
studies describing that physical activity affects this domain of cognition outside of pain sensation
(Cooper et al., 2016).
Motor skills
Motor skills include several different basic elements of motor activity such as fine motor
abilities, reaction time, and global skills such as balance (Harvey, 2019). The ability to
accurately produce the force required to control different movements that vary in magnitude,
speed and precision is accomplished through motor control (Perrey, 2013). Muscles act in
response to neural commands to produce the required range of motor outputs. With the
development of the neuroimaging techniques like computerized tomography, magnetic resonance

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imaging (MRI), and transcranial magnetic stimulation (TMS), it is clear to visualize that aerobic
training of muscular and motor skills leads to better locomotion and motor skills (Kamp et al.,
2014).
Memory
Referencing an extensive meta-analysis on memory and physical activated published by
Roig et al. 2013, cardiovascular exercise has a small effect on memory (Roig et al., 2013). A
more stratified analysis of the memory outcomes showed that while acute aerobic interventions
produced a relatively large improvement in some aspects of long-term and short term memory,
long-term aerobic exercises had no significant effects on long-term memory and produced only
small improvements in some aspects of short-term memory (Roig et al., 2013). Although studies
do not generally show significant increases in memory resulting from long-term exercise
intervention, neurophysiological adaptations that maintain brain structures that affect memory
are seen. For example, cross-sectional studies have established associations between the level of
cardiovascular fitness and the volume of areas of the brain, such as the hippocampus and basal
ganglia which are involved in certain types of memory (Roig et al., 2013). It is important to note
our current standard methods for assessing memory are not as sensitive as tests assessing other
domains of cognition.
Attention and Executive Functioning
Executive function is commonly referred to as reasoning or problem solving (Harvey,
2019). It is the most consistently identified domain of cognition to benefit from physical activity.
Colcombe and Kramer’s meta-analysis analyzed 18 exercise intervention studies between 1966

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and 2001 looking at the influence of physical activity on cognitive performance in people aged
55 years or older (Colcombe & Kramer, 2003). They found that the biggest positive influence of
physical activity was on executive control functions (Voelcker-Rehage & Niemann, 2013).
Colcombe et al. first conducted a MRI study suggesting that after cardiovascular training, older
adults applied cognitive resources more effectively and cognition was improved (Colcombe &
Kramer, 2003). Using a modified Flanker task, they showed significantly higher brain activation
for physically active older adults compared to inactive older participants in different frontal and
parietal regions and significantly lower activity in the anterior cingulate cortex. The same was
true for older adults participating in a 6-month aerobic walking exercise intervention compared
to a stretching control group (Kramer et al., 2003). Frontal structures contribute to a range of
high-level cognitive functions including attentional selection, working memory, task switching,
and inhibitory control. This study suggest that higher physical activity levels is related to higher
cognitive performance and brain activation in frontal, and parietal areas. Conversely, some
studies have found reduced frontal activation after training in at least some regions that have
been activated prior to training. In the cognitive aging literature, these contradictory findings are
explained in that on the one hand, increasing task load is associated with increased recruitment.
On the other hand, increased efficiency in the processes subserved by these regions might lead to
reduced activations.
Processing speed
Exercises has been shown to be effective in reversing, or slowing age-related declines in
processing speed (Rikli & Edwards, 1991). Colcombe and Kramer’s 2003 meta-analysis
analyzed 18 exercise intervention studies between 1966 and 2001 looking at the influence of
physical activity on cognitive performance in people aged 55 years or older (Colcombe &

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Kramer, 2003). They found that exercise interventions significantly improved measures of
processing speed (Colcombe & Kramer, 2003).
Language/verbal skills
In an intervention performed by Pereira et al. 2007, cerebral blood volume was measured
in adults (mean age = 33 years) following 12 weeks of aerobic exercise training. This exercise
selectively influenced the dentate gyrus, with cerebral blood volume changes correlating with
cardiorespiratory fitness changes. Following the exercise intervention, these findings were
coupled with improved cognitive performance on the Rey Auditory Verbal Learning Test
(Pereira et al., 2007).
Exercise Induced Neuroplasticity
Neuroplasticity describes the brain’s ability to encode experiences and learn new
behaviors by reorganizing itself structurally and physiologically through angiogenesis,
synaptogenesis, and neurogenesis mediated at the molecular level by the brain-derived
neurotrophic factor (BDNF), insulin-like growth factor 1 (IGF-1), vascular endothelial growth
factor (VEGF), nerve growth factor (NGF), hormones and second messengers (Rashid et al.,
2020). While neuroplasticity occurs naturally throughout all stages of life, it has become clear
that exercise can induce neuroplasticity. PA most directly supports these supramolecular
processes by increasing the blood supply to the brain. PA promotes angiogenesis that increases
the vascular supply of the brain, which ultimately makes the brain grow healthily. Furthermore,
it is has well understood that angiogenesis increases cerebral blood volume which promotes
neurogenesis. Pereira et al. (2007) provided first evidence in a combined human–animal study

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that the enhancement of cerebral blood volume in the dentate gyrus was in line with an increased
number of newborn cells within the rat dentate gyrus. Finally, it is a general consensus that
continuous physical activity increases the expression of genes concerned with synaptogenesis,
which is associated with an increase in cognitive functions of the brain (Rashid et al., 2020).
Thomas et al. provided time courses of the different components of brain plasticity
(capillary density, neurogenesis, astrocyte volume and neuropil volume) and the types of
exercise they react to (Thomas et al., 2012). Changes in capillary density have a very rapid time
course in animals; they can occur within 3 days after increased aerobic activity onset and return
to baseline within 24 hours of sedentary behavior (Van Der Borght et al., 2009). They also react
to environmental enrichment. Neurogenesis is often coupled with angiogenesis following a
corresponding time course. Furthermore, it has been shown that new neurons will die if adequate
learning opportunities or novel experiences do not accompany the increase in cardiovascular
activity (Van Praag et al., 1999). Changes in astrocyte volume seem to be related to
environmental enrichment and not to cardiovascular activity in that astrocytes return to baseline
volume as soon as activity is stopped (Kleim et al., 2007). One might conclude that the “vascular
component” of the brain response may be driven by aerobically challenging cardiovascular
exercises whereas the “neuronal component” may reflect cognitively engaging motor learning
(Churchill et al., 2002).
Reiterating that PA and years of cognitively engaging learning are the only modifiable
risk factors for age-related cognitive decline in healthy adults, it is interesting to find that these
factors target different mechanisms of neuroplasticity. Thus, in my following review, I will be
studying literature of the synergistic interventions of PA and cognitive engagement.

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PART 3: COGNITIVE + AEROBIC EXERCISE INTERVENTION
Introduction

Increase in human lifespan and sedentary lifestyle influenced by technological
advancement are exacerbating the prevalence of age-associated cognitive decline worldwide
(Kohl et al., 2012; Lazarus & Harridge, 2018); yet there is no medical treatment for this decline.
The declining trend begins for adults in their 20s and accelerates at age 50. Despite the profound
effect of aging on cognitive performance, considerable variation in cognitive performance is still
clearly observed at all ages. These individual differences raise the possibility that specific types
of activities may contribute to enhanced cognition. It has not yet been determined how physical
activity influences cognitive outcomes, but the positive effects on health have been well
documented. It is important to understand how physical activity can be utilized to attenuate
cognitive decline because cognition is indicative of daily function and quality of life, especially
in older adults (Baumgart et al., 2015). Promising research has revealed that physical activity
induced adaptations in neuroplasticity may mediate cognitive improvements through the
facilitation of exercise-induced neurotrophic factors (Jakowec et al., 2016). Neuroplasticity
describes the brain’s ability to encode experiences and learn new behaviors by reorganizing itself
structurally and physiologically (Petzinger et al., 2013). Growing evidence supports that there is
an experience-dependent neuroplasticity that requires an environment that is simultaneously (1)
cognitively engaging, promoting alteration of neural synapses (synaptogenesis), and (2)
aerobically challenging, facilitating improved circulatory function in the brain (angiogenesis)
which both promote neurogenesis (Petzinger et al., 2013). These types of interventions have not

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been optimized in humans because they are currently investigated with exercise that does not
have sensory-motor and cognitive procedures that can be personalized (Canning et al., 2020).
Specifically, while intensity of motor tasks can be personalized, there is no standard procedure
for personalized loading of cognitive activity (Jakowec et al., 2016).
Executive Function

Executive function (EF) is the most consistently identified domain of cognition to benefit
from training (Harvey, 2019). The term EF refers to the higher-level cognitive skills you use to
control and coordinate other cognitive abilities and behaviors (Harvey, 2019). There are three
core sub-domains: (1) inhibition (2) working memory, and (3) cognitive flexibility (Harvey,
2019). From these, higher order EFs are built such as reasoning, problem solving, and planning
(Harvey, 2019). EFs are skills essential for mental health, physical health, and quality of life
(Diamond, 2013). For these reasons, EF will be the primary area of cognition reviewed because
of its applicability in multiple task contexts.
The aforementioned neuroplasticity allows neurons and their circuits within the brain to
compensate for injury and disease and to adjust their activities in response to changes in their
environment (Jakowec et al., 2016). While the most robust period of neuroplasticity as
influenced by activity and the environment occurs during a narrow developmental window in
early life called the critical period, it is now recognized that this window may never close, and
mechanisms are beginning to be identified by which we can re-open this developmental window
in the adult when the critical period has passed (Jakowec et al., 2016) .

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EF and Aerobic Training
A physically active lifestyle seems to have a positive influence on brain structure and
brain function (Gomez-Pinilla & Hillman, 2013). Hence, research on the role of physical activity
to executive functions shows promise. Engaging in repetitive aerobic physical activity influences
general brain health by increasing blood flow through vascularization and angiogenesis,
induction of neurotrophic factors, and promoting neurogenesis (Vecchio et al., 2018).
EF and Cognitive Training
There are several longitudinal studies that have used different types of cognitive training
programs to improve EF (Scionti et al., 2020). The idea that keeping mentally active helps
maintain one’s cognitive functioning is also known as the “use it or lose it” hypothesis, though
the hypothesis is much better received by the general public than academia (Salthouse, 2006).
Novel and challenging cognitive activities such as learning a foreign language or a musical
instrument, have been suggested to mitigate age-related cognitive declines (Nijmeijer et al.,
2021). The controversy of the “use it or lose it” hypothesis is whether new learning experiences
are transferable to other cognitive tasks. In general, cognitive training does not easily transfer,
and when it does, near transfer is much more often observed than far transfer (Sala et al., 2019).
In near transfer, the effect of learning is transferred to tasks that share high degrees similarity
with the training task. In contrast, transfer performance on a far transfer lack of any similarity
between the transfer and learning task (Sala et al., 2019). The demonstration of far transfer is
highly desirable because transferring the learning experience to non-trained tasks or non-targeted
performances is the premise of the “use it or lose it” hypothesis that motivates the practice and

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research of cognitive intervention. Therefore, there is enormous interest in learning about the
conditions for successful far transfer in cognitive training. Hertzog et al. suggested that EF
training oriented interventions produced more satisfactory transfers (Hertzog et al., 2008). Rather
than focusing on micro-level processes and task-specific strategies, these training regimes
targeted mechanisms used by individuals to exert cognitive controls in multiple task contexts,
which could be directly related to successful transfer performance.
Maximizing EF through Training
Growing evidence supports a synergistic amplification in EF benefits when aerobic
training is paired concurrently with cognitive training (Ploughman et al., 2019). How to integrate
motor and cognitive demands in physical training represents a recently growing line of research
across the lifespan in exercise science. In aging research, specific forms of dual task training
with cognitive-motor interference have been developed and demonstrated to benefit cognitive
performance (Falbo et al., 2016).
VR Exergaming Interventions
VR technology is a laboratory tool that can be used as a dosed analogue to real world
experiential activities necessary for exercise-induced synaptogenesis (Qian et al., 2020). Recent
study findings suggest that VR exercise training may provide positive cognitive health benefits
and better outcomes than no treatment, traditional exercise, or rehabilitation interventions for
daily function and balance (Canning et al., 2020). In 2016, a randomized, controlled trial at five
clinical centers across five countries found that aerobic treadmill training was an effective
intervention that targeted motor and cardiovascular deficits (angiogenesis) in order to mitigate

Manipulating Cognitive Intensity During Aerobic Exercise
54
fall risk in older adults, but did not target cognitive deficits (synaptogenesis) (Sun et al., 2021).
These researchers found that when goal based training components were integrated into treadmill
training using VR in order to target cognition, fall outcomes mediated by EF were significantly
better than treadmill training alone (Sun et al., 2021). Currently, these types of investigations are
scarce.
Exergaming, or active video gaming, is emerging as a means to facilitate the VR
mediated goal oriented exercise interventions (Costa et al., 2019). Exergaming is defined as
digital games that require bodily movements to play, stimulating an active gaming experience to
function as a form of physical activity (Benzing & Schmidt, 2018).

Limitations
A major limitation is that when VR interventions occur, the vast majority of these
interventions challenge participants cognitively but not aerobically (Canning et al., 2020). Also
many of these studies include protocols that are not immersive, but greater immersivity is
considered a key element in achieving embodied simulations and inducing engagement (Canning
et al., 2020). Further, currently there is no standard schema for any domain of cognition in the
field to grade cognitive difficulty imposed on the individual. Table 5 outlines all clinical
interventions in this area.

Manipulating Cognitive Intensity During Aerobic Exercise
55
Table 5. Outlined Literature Review of Aerobic + Cognitive Clinical Interventions to Improve Cognition Title Author
Year
Aerobic task
Aerobic task
personalized
Cognitive
Task
Cognitive
Task Concurrent Personalized training
Population
Age
Effects of
combined
intervention of
physical
exercise and
cognitive
training on
cognitive
function in
stroke survivors
with vascular
cognitive
impairment: a
randomized
controlled trial
(Bo et al., 2019)
2019
jogging/cycling
yes, participants
monitored for
moderate
training zone
using Borg
scale
COGPACK
program
No
no, cognitive
before aerobic
Stroke
older adults
Randomized Trial
of Combined
Aerobic,
Resistance, and
Cognitive Training
to Improve
Recovery From
Stroke: Feasibility
and Safety
(Koch et al., 2020)
2020
Training
Stationary
treadmill / bicycle
ergometer (sitting
or recumbent)
yes, 50% to 55%
of the individuals
estimated
maximum heart
rate for aerobic
exercise and were
gradually
increased to 65%
as tolerated
an adaptive
computerized
platform from
Brain Fitness
Program, Posit
Science, San
Francisco, CA
no
no, cognitive
training done after
physical activity
Stroke
older adults
A
Randomized
Controlled
Trial of a
Walking
Training with
Simultaneous
Cognitive
Demand (Dual
Task) in
Chronic
Stroke.
(Meester et
al., 2019)
2019
walking
Aerobic
training zone,
between 55%
and 85% of
the agepredicted
maximum
heart rate
cognitive
tasks, a
listening task
or talking
about
planning daily
activities
no
yes
Stroke
older adults
Synergistic Benefits of
Combined Aerobic and
Cognitive Training on
Fluid Intelligence and the
Role ofIGF-1 in Chronic
Stroke
(Ploughman et al., 2019)
2019
treadmill
target heart rate zone
corresponding to 60% to
80% of peak oxygen
uptake (VO2peak)
Computerized dual-n-back
training was performed
with level of difficulty
adapted to the individuals
performance. The working
memory task involved
monitoring a series of 2
concurrent stimuli and
indicating whether the
current stimuli match those
presented n items back in
the series. Value of n
started at 1 and gradually
increased, dependent on
participant performance
yes
no
Stroke
older adults
The Active
Ingredient of
Cognitive
Restoration:
A
Multicenter
Randomized
Controlled
Trial of
Sequential
Combination
of Aerobic
Exercise and
ComputerBased
Cognitive (Yeh et al., Training in 2019) Stroke
2019 Survivors
With aerobic
Cognitive resistance
Decline. exercise
target heart
rate during
the aerobic
period was
40% to 70%
of the
patient’ s
maximal
heart rate
30 minutes
of computerbased
cognitive
training with
the Brain HQ
program,
yes, the
training no, aerobic program was then adjusted cognitive automaticall
Stroke y and
continuously
according to older adults each
participant’
s level of
performance
The effect of
physical activity
with and without
cognitive demand
on the improvement
of executive
functions and
behavioral
symptoms in
children with
ADHD
(Nejati &
Derakhshan, 2021)
2021
running
no
Exercise for
Cognitive
Improvement and
Rehabilitation
(EXCIR). The
EXCIR consists of
12 exercises with
progressive
cognitive demands
at 10 levels to
improve cognitive
functions such as
attention, cognitive
flexibility,
inhibitory control,
and working
memory
no
no
ADHD
adolescent
Cycling and
Spatial
Navigation in
an Enriched,
Immersive
3D Virtual
Park
Environment:
A Feasibility
Study in
Younger and
Older Adults
(Sakhare et
al., 2019)
2019
cycling
no
spatial
navigation
tasks were
assessed by
total cycling
time, mean
cycling
speed, and
percentage of
correct
decisions in
the virtual
environment
no
yes
Healthy
Adults
young adults
older adults
Walking in
fully
immersive
virtual
environments:
an evaluation
of potential
adverse
effects in
older adults
and
individuals
with
Parkinsons
disease
(Kim et al.,
2017)
2017
treadmill
yes
virtual reality
environment
exposure
no
yes
Healthy
Adults
Parkinson's young adults Patients older adults

56
CHAPTER IV: COGNITIVE INTENSITY CAN BE SYSTEMATICALLY
MANIPULATED DURING AEROBIC EXERCISE
ABSTRACT
Alongside an increase in human lifespan over the last several decades, technology has
encouraged an increasingly sedentary lifestyle. This has exacerbated the prevalence of cognitive
decline associated with an aging population. Purpose: The purpose of this project is to
determine if executive function can be progressively loaded using multimedia delivery, time
constraint, and dual task environments. Methods: A total of 40 recruited participants were
equally allocated into two groups according to age (18-29: young adult; 65-75: older adult).
After a collection of baseline descriptive data and acquisition to the Wisconsin Card Sorting
Task (WCST64), participants completed 7 trials of the WCST64 under conditions with
manipulated variables (time constraint, complexity, dual tasking). A 10 Meter Walk test was
collected both before and after intervention. Cognitive performance was measured by the
number of errors in the WCST64; gait performance was measured by changes in average gait
speed and stride length as compared to baseline. A paired t-test was used to assess the difference
between the conventional and digital version of the WCST64; the effect of progressive load (i.e.,
time constraint, number of sorting option, etc.) on WCST64 score while stationery and walking
was assessed using a mixed-effect linear model. Results: There was no significant difference
between the traditional and digital delivery of the WCST64 (p=0.152), and this difference did not
vary by age group (p=0.845). There was a significant difference in errors across all four
conditions while stationary and while walking (both p<0.0001). Additionally, the conditional

Manipulating Cognitive Intensity During Aerobic Exercise
57
effect was significantly dependent on age group (p=0.008; p=0.047 respectfully). Stride length
and gait speed were both significantly different across conditions (p=0.0003; p<0.0001
respectfully). Significance: These results support the development of progressively loading
cognitive tasks during aerobic + cognitive exercises that may improve EF, particularly as a
preventative intervention for age associated cognitive decline.
INTRODUCTION
Increase in human lifespan and sedentary lifestyle influenced by technological
advancement are exacerbating the prevalence of age-associated cognitive decline worldwide
(Kohl et al., 2012; Lazarus & Harridge, 2018); yet there is no medical treatment for this decline.
There is, however, a specific intervention that has the potential to attenuate this problem that has
not yet been optimized in humans. It has not yet been determined how physical activity
influences cognitive outcomes, but its positive effects on this domain of health have been well
documented (Baumgart et al., 2015). It is important to understand how physical activity can be
utilized to attenuate cognitive decline because cognition is indicative of daily function and
quality of life, especially in older adults (Wollesen et al., 2020). Promising research has revealed
that physical activity induced adaptations in neuroplasticity may mediate cognitive
improvements through the facilitation of exercise-induced neurotrophic factors (Jakowec et al.,
2016). Neuroplasticity describes the brain’s ability to encode experiences and learn new
behaviors by reorganizing itself structurally and physiologically (Petzinger et al., 2013).
Growing evidence supports the idea of an experience-dependent neuroplasticity that requires an
environment that is simultaneously (1) cognitively engaging, promoting alteration of neural

Manipulating Cognitive Intensity During Aerobic Exercise
58
synapses (synaptogenesis), and (2) aerobically challenging, facilitating improved circulatory
function in the brain (angiogenesis) which both promote neurogenesis (Jakowec et al., 2016;
Rashid et al., 2020). When combining aerobically challenging movement with cognitively
challenging tasks, this dual tasking forces a split in attentional resources making the combination
more demanding than either approach alone. These types of interventions have not been
optimized in humans because they are currently investigated with exercise that does not have
sensory-motor and cognitive procedures that can be personalized (Canning et al., 2020).
Specifically, while intensity of angiogenesis-related motor tasks can be personalized through
mechanisms such as increased speed, there is no standard procedure for personalized loading of
synaptogenesis-related cognitive activity (Jakowec et al., 2016). Currently, the way that
cognitive challenge is implemented during aerobic + cognitive exercise is not well established.
However, the Cognitive Load Theory (CLT) actually defines cognitive load and presents a
theoretical framework for how it can be manipulated (Deng et al., 2021). CLT defines cognitive
load as a multi-dimensional construct representing the load that performing a particular task
imposes on a learner's cognitive system. Theoretical task characteristics that have been identified
in CLT research to manipulate cognitive load are task format, time constraint, task complexity,
use of multimedia, pacing of instruction, and dual tasking. Although this literature exists, it
hasn’t been incorporated in aerobic + cognitive interventions. As investigations that employ
aerobic + cognitive interventions to target cognitive improvements become more common,
researchers must know to what degree a cognitive task is challenging an individual and how to
progressively scale these challenges to optimize these interventions. As initial steps, researchers
must first show that cognitive tasks can be progressively loaded during these aerobic + cognitive
interventions.

Manipulating Cognitive Intensity During Aerobic Exercise
59
PURPOSE
To address this gap in knowledge, we propose an approach to challenge the executive
function (EF) domain cognition in a range of healthy adults using a digital cognitive task. Our
goal is to use this intervention to (1) determine if cognition in the EF domain can be
progressively loaded and to (2) create a standard definition for cognitive loading of an EF task.
In a healthy central nervous system, the ability to process information is limited and one’s
capacity to flexibly and efficiently make use of available mental resources is referred to as
cognitive reserve (M. Tucker & Stern, 2011; Malcolm et al., 2015). When task requirements
exceed the capacity of one’s cognitive reserve, the system balances cognitive demands by
switching attention to the most task-relevant information as it becomes available (Harvey, 2019).
Models such as Fitt’s law of speed-accuracy trade-off provide indirect guidance for how load can
be intensified for motor task by demonstrating how decreased time to complete a task decreases
performance defined by accuracy (Mackenzie & Isokoski, 2008). Our research aims to determine
if such a motor paradigm could hold true in the cognitive domain to elicit increased cognitive
load using insights from CLT. In essence, can a systematic decline in cognitive task be
demonstrated in the same way that is has been demonstrated in motor tasks through the
manipulation of parameters of the tasks? In the literature, cognitive load has been measured
using brain activity patterns measured by functional near-infrared spectroscopy (fNIRS),
electroencephalogram (EEG), gaze behaviors measured by eye-tracking, gait variability and
changes in overall walking speed, performance decrement on specific cognitive tasks, and
subjective surveys (Ho et al., 2019; Mackenzie & Isokoski, 2008; Parbat & Chakraborty, 2021).
While these methods exist to measure cognitive load, there is currently no consistent protocol to

Manipulating Cognitive Intensity During Aerobic Exercise
60
prescribe cognitive load in any aerobic + cognitive exercise intervention. With this intervention,
we hope to identify such a protocol.
Past research that has identified EF as the most consistently identified domain of
cognition to benefit from cognitive training has informed our decision to use EF to establish such
a paradigm (Harvey, 2019). In Colcombe and Kramer’s meta-analysis analyzed of 18 exercise
interventions between 1966 and 2001 looking at the influence of physical activity on cognitive
performance in older adults, they found that the biggest positive influence to be on EF
(Colcombe & Kramer, 2003; Voelcker-Rehage & Niemann, 2013). The idea that keeping
mentally active helps maintain one’s cognitive functioning is known as the “use it or lose it”
hypothesis (Voelcker-Rehage & Niemann, 2013). Novel and challenging cognitive activities
such as learning a foreign language or a musical instrument have been suggested to mitigate agerelated cognitive declines (Wang et al., 2011). One controversy of the “use it or lose it”
hypothesis is whether new learning experiences are transferable to other cognitive tasks. In
general, cognitive training does not easily transfer, and when it does, near transfer is much more
often observed than far transfer (Wang et al., 2011). Hertzog et al. suggested that EF training
interventions produce more satisfactory transfer outcomes because rather than focusing on
micro-level, task-specific strategies, these training interventions target mechanisms used by
individuals more globally, which could be directly related to successful transfer performance
(Hertzog et al., 2008). This supports our decision to focus on EF in our research because this
cognitive domain depends on efficient coordination of activity across all cognitive domains (M.
Tucker & Stern, 2011). In our research, it is important to study such a paradigm in both older and
younger individuals because researchers have identified that EF performance generally declines
in the transition from young adulthood to older adulthood as a result of age-related functional

Manipulating Cognitive Intensity During Aerobic Exercise
61
and structural connectivity changes in the brain (Fjell et al., 2016). Thus, in our research we
identify healthy younger adults (18-29 years old (Geiger & Castellino, 2011) and older adults
(65-75 years old (Borelli et al., 2018)) for participant recruitment to analyze age as a potential
covariate in this paradigm.
For our research, the cognitive task that we manipulate is the Wisconsin Card Sorting
Test (WCST) which is a common cognitive task used to broadly assess EF in research and
clinical practice (Miles et al., 2021). The standard 128 trial WCST can be a time-consuming
process; therefore, we use the validated short form WSCT64 for our research (Greve, 2001). This
procedure retains all the features of the standard WCST except length (Greve, 2001). For our
aims needing to pair aerobic activity with this cognitive task, we utilize walking. We specifically
have our participants walk on a Blue Goji Treadmill which is outfitted with (1) a screen to
deliver a digital WCST64, (2) load sensors for kinetic data, and (3) cameras for kinematic data
(Blue Goji Operations Manual). The ability to analyze the kinetic and kinematic data of our
participants during their trial will be useful because EF demands that overwhelm their available
cognitive reserve has been associated with decreased walking speed, and increased stride
variability (Ijmker & Lamoth, 2012).
METHODS/DISSERTATION APPROACH
Participants and Study Design
For our study, young adults were recruited using flyers around the USC Division of
Biokinesiology and Physical Therapy. Older participants were also recruited using flyers around
the USC Division of Biokinesiology and Physical Therapy. This proof-of-concept study enrolled

Manipulating Cognitive Intensity During Aerobic Exercise
62
(40) equally allocated into two groups according to their respective age groups, 18 to 29 and 65
to 75 years old. There is little support for significant sex differences in EF so gender was not
stratified in recruitment (Grissom & Reyes, 2019). Interested participants were contacted by
primary investigator by phone to confirm eligibility. Interested eligible participants reported to
the Clinical Exercise Research Center (CERC) of the USC Division of Biokinesiology and
Physical Therapy to review and sign the informed consent document. All procedures of this
single-group clinical trial were conducted at the University of Southern California (USC, Los
Angeles, CA) Health Sciences Campus. This study was approved by the USC Health Sciences
Review Board and registered with ClinicalTrials.gov. A visual outline of the intervention
procedures is shown in Figure 15. Written informed consent was obtained from all potential
participants assessed for eligibility. Primary considerations for inclusion/exclusion criteria were
to ensure that participants were healthy, interested, and available to participate, without any
contraindications to the intervention procedures. Specifically, participants were required to meet
the following criteria: 18-29 or 65-75 years of age; available for study visits; competent in
English sufficient for assessment and training; answer ‘NO’ to all questions on the Physical
Activity Readiness Questionnaire (PAR-Q) or receive medical clearance from a physician;
cognitively healthy (no impairment identified in baseline WCST64); have no known history of
cardiovascular or metabolic disease or chronic illness which may compromise the patient’s
ability to safely perform the aerobic exercise; have no medical implant devices such as
pacemakers; and have no musculoskeletal injuries or medical conditions for which exercise is
contraindicated.

Manipulating Cognitive Intensity During Aerobic Exercise
63
Figure 15. Diagram of participant recruitment and testing
This is a proof-of-concept clinical trial, so sample size was determined based on
feasibility and study funding. To determine the effect that delivery method (traditional card
based WCST64 vs Blue Goji digital WCST64) has on performance of a WCST64 in older and
younger adults, based on an equivalence test of means from a preliminary pilot study using two
one-sided paired t-test design, assuming the actual mean of paired difference is 1.00 with the
standard deviation of paired difference is 2.50. The preliminary pilot study used has 81% study
power to detect the equivalence limits of -2 and 2 between standard and digital WCST64. To
determine the effect that different combinations of time constraints and number of sorting
options (complexity) had on performance of a WCST64 in older and younger adults, a single
factor, repeated measure design with a sample of the same sample size (40 adults), measured at 8
times with different conditions and assuming the standard deviation across subjects at the same
time point is 8 with a correlation of 0.50 between measurements has 60% study power to detect
an effect size of 0.73 among means based on Wilks Lambda test. These calculations are based on
the preliminary pilot information. There was no study power to detect the effect of the
Assessed for eligibility (n=40)
Included in study (n=40)
Completed baseline testing (n=40)
Completed acquisition (n=40)
Completed intervention (n=40)
Completed post-intervention fatigue test (n=40)

Manipulating Cognitive Intensity During Aerobic Exercise
64
condition’s influence on the association between gait performance and cognitive performance
with a sample based on our preliminary pilot data, thus this serves as a clinical proof-of-concept
for this aim. Significance level is set at 0.05 throughout this analysis.
Intervention Procedures
Assessments performed exclusively to determine eligibility for this study were done only
after obtaining informed consent. Descriptive factors to be collected include age, body
composition, gender, aerobic capacity, chronic pharmacological treatment, and treated
pathologies. All measurements were collected during a single study visit. At the start of the visit,
participants laid supine for 5 minutes to reach a resting state to measure the resting heart rate
(HR) using a HR monitor. This resting HR was used along with age predicted HR max to
determine HR reserve (HRR). After baseline testing, participants were given an acquisition
period to (1) master the WCST64 and (2) to identify target walking pace on the Blue Goji nonmotorized treadmill. The WCST64 acquisition consisted of 4 randomized, consecutive trials of
the stationary WCST64 (2 traditional, 2 digital). The Blue Goji treadmill acquisition included
identification of a comfortable resistance and the memorization of a walking pace that elicits
30%-50% of HRR. After treadmill acquisition, participants completed a baseline 10 Meter Walk
Test. After this, participants will complete the digital WCST64 under 7 additional conditions
with manipulated variables (time constraint, complexity, dual tasking). Participants ended data
collection with a post intervention 10 Meter Walk Test.

Manipulating Cognitive Intensity During Aerobic Exercise
65
Primary Outcomes
Cognitive Performance:
Errors on the Wisconsin Cart Sorting Task 64 (WCST64) were used as the primary
measure of executive function performance. The Wisconsin Cart Sorting Test (WCST) is a wellestablished measure of executive function, and the WCST64 is a valid short form version of the
test. Published t-scores were used for the counted errors to allow for data analyses that accounted
for age and education level across all participants. All traditional forms of the WCST64 were
performed standing with the typical card-based method. Participants were asked to match each of
the cards in the deck to one of these four key cards placed on a table in front of them.
Participants took the top card from the deck and placed it below the key card they thought it
matched. Participants were verbally told by the research assistant if their answers were right or
wrong each time. If they were wrong, they were instructed to leave the card where they placed it
and try to get the next card correct. All digital forms of the WCST64 were administered
standing/walking on the Blue Goji non-motorized treadmill. Participants were asked to match
each of the cards that showed up at the bottom of the screen to one of the four key cards at the
top of the screen. To select a choice, participants used to touch screen to point to their choice. A
green check mark appeared each time they were right and a red “X mark” appeared each time
they were wrong.
Gait Performance:
Gait used to be generally viewed as a largely automated motor task, requiring minimal
higher-level cognitive input. Increasing evidence, however, links executive function and gait due
to the fact that locomotion utilizes some of the same, limited mental resources as EF (Yogev-

Manipulating Cognitive Intensity During Aerobic Exercise
66
Seligmann et al., 2008). The Blue Goji non-motorized treadmill was used for all gait data
collection. The Blue Goji treadmill was outfitted with 4 load sensors that sampled at 50 Hz.
Bilateral gait speed, and bilateral stride length were measured for each step. Stride variability
was collected. Our operational definition of gait performance is variability from average gait
speed and stride length during the baseline condition, as there is research that supports that gait
speed and gait variability are affected during dual task walking (Beurskens & Bock, 2012; Li et
al., 2001; Lindenberger et al., 2000) .
Secondary Outcomes
Body Composition:
Body composition was assessed in morning fasted conditions. Weight (lbs), BMI, percent
body fat (PBF, %), and lean body mass (lbs) were measured on an InBody 770 (InBody, Seoul,
South Korea).
Questionnaires:
The SF-36 is a multipurpose, short-form health survey with only 36 questions. It yields an eightscale profile of scores as well as physical and mental health summary measures (physical
functioning, role of limitations due to physical health, role of limitations due to emotional health,
energy/fatigue, emotional well-being, social functioning, pain, general health, health change
(Quaytman, 2000). There was a potential that these factors could confound performance in the
intervention, so it was important to measure them.
Estimated VO2max Testing:
The 4 Minute Walk Test was used to estimate aerobic capacity of participants because
this is an important element of success in many activities (Shete, 2014). Participants completed a
submaximal treadmill exercise protocol to estimate maximal oxygen consumption (VO2max), as

Manipulating Cognitive Intensity During Aerobic Exercise
67
a measure of aerobic capacity. A polar heart rate monitor will be used to measure heart rate
during the test. Participants warmed-up for 2 minutes at a 0% grade and a walking speed
(recommended speed: 3.0-4.5 mph) that brings their heart rate to 50%-70% of their age-predicted
max heart rate. After the warmup, the treadmill grade will be increased to 5%, and the participant
will continue to walk at the same speed for an additional 4-minutes. Steady-state (SS) heart rate
will be recorded. The formula for estimated VO2max is: Estimated VO2max (ml·kg-1·min-1) =
15.1 + 21.8 (speed) – 0.327 (SS heart rate in bpm) – 0.263 (speed x age) + 0.00504 (SS heart rate
x age) + 5.98 (gender; female = 0, male = 1).
Fatigue Testing:
The 10 Meter Walk Test used a pre/post measure to determine fatigue development
(Dalgas et al., 2012). The 10 Meter Walk Test is a performance measure used to assess walking
speed in meters per second over a short distance. The test can be used to identify changes in gait
speed in response to therapeutic interventions. We used this a pre/post measure to determine if
our intervention fatigued participants.
Statistical Analyses
The purpose of this analysis was to (1) evaluate the equivalence between traditional and digital of
Wisconsin Card Sorting Test 64, WCST64, (2) to assess the difference of combinations of time
constraints, number of sorting option at stationary and walking on performance of digital WCST64, (3)
and to assess the correlation between gait performance and cognitive performance.
Demographic and socio-economic characteristics distributions are described using frequency and
percentage for categorical variables. Continuous variables are described in mean and standard deviation.
In study aim 1, the difference of performance scores between traditional and digital of WCST64 were
compared using paired t-test. Then, the equivalence margin was evaluated by comparing the predefined

Manipulating Cognitive Intensity During Aerobic Exercise
68
equivalence limits of -2 and 2 from sample size and power analysis with the mean difference of the 95%
confidence interval which shown in Figure 16. Non-additive effect (i.e., whether the differential effect of
delivery method on WSCT64 performance was depending on age group) was assessed by including
delivery method and age group interaction term in mixed-effects linear model.
For study aim 2, the effect of time constraints, number of sorting options (complexity), and age
group at stationary on digital WCST64 error T-scores was assessed univariately using mixed-effects
linear model. Besides that, non-additive effect was assessed for example whether time constraints on
digital WCST64 performance scores varied by age group by including the time and age group interaction
term in the model.
In study aim 3, the effect of activity (walking versus standing), time constraints, number of
sorting options (complexity), and age group at walking on digital WCST6 error T-scores was assessed
univariately using mixed-effects linear model. Non-additive effect was assessed whether activity, time
constraints, complexity, or age group was varied by age group, time, activity, or complexity by including
interaction term in the model one at a time.
The mean of stride length and gait speed of these 40 participants was also assessed using mixedeffects linear model. Simes’ false discovery rate was used for multiple pairwise comparisons to assess the
combinations pairwise differences. In addition, whether the combinations of time constraints, number of
sorting options at walking on error T-scores of digital WCST64, and the mean of stride length and gait
speed were differed by age group was assessed by including the combination and age group interaction
term in the model.
Then, the difference across conditions in the mean of both stride length and gait speed from
baseline was assessed using mixed-effects linear model. The correlation of the percent change in total
errors of digital WCST64 and the percent change in the mean of stride length mean and gait speed for
each condition from the baseline and by age group was evaluated using spearman correlation and scatter
plots. The significance level was set at 0.05 with a two-sided test throughout the analysis. All statistical

Manipulating Cognitive Intensity During Aerobic Exercise
69
computations were done in Stata/SE 17.0 (StataCorp, College Station, TX, USA).
RESULTS
Descriptive Data
Among these 40 participants, the age groups were equally distributed and 57.5% were
female and 42.5% were male. Majority of these participants were black (42.5%) and white
(32.5%) and the mean of their years of school was 17.25 years (Table 6).
Table 6. Demographic and socio-economics characteristics
Variables Total N = 40
Age group, n (%)
18-29 20 (50)
64-75 20 (50)
Sex
Female 23 (57.50)
Male 17 (42.50)
Ethnicity
White 13 (32.50)
Black 17 (42.50)
Asian 5 (12.50)
Multi-ethnic 5 (12.50)
Years of school 17.25 (1.79)
Traditional vs Digital WCST64
The purpose of this analysis is to evaluate the equivalence between traditional and digital
of Wisconsin Card Sorting Test 64, WCST64. Table 7 shows the mean difference of
performance between traditional and digital WCST64. According to paired t-test, there was no
significant difference in mean error T-scores between traditional and digital WCST64 (p =
0.152). Performances of the two delivery methods did not appear to be equivalent as the 95%
confidence interval of mean difference -2.62 and 0.42 was not within the predefined equivalence

Manipulating Cognitive Intensity During Aerobic Exercise
70
limits of -2 and 2 as shown in Figure 16. Though there was no significant difference between the
two delivery methods, the non-equivalence could be due to small sample size as the 95%
confidence interval of the mean difference appeared to be wide. Besides that, the difference
between the two delivery methods did not differ by age group (interaction p = 0.845) (Table 8).
Figure 16. Summary on equivalence evaluation of digital WCST64
Table 7. The distribution and difference between traditional and digital WCST64 using paired ttest
Error T-scores
Mean (SD)
Mean difference (95% CI) P
Traditional WCST64 52.75 (11.13) -1.10 (-2.62, 0.42) 0.152 Digital WCST64 51.65 (10.30)

Manipulating Cognitive Intensity During Aerobic Exercise
71
Table 8. The distribution and difference of error T-scores between traditional and digital
WCST64 by age group
Age group 18-
29
Mean (SD)
Age group 64-
75
Mean (SD)
Difference in delivery method by age
group
p
Traditional
WCST64
54.25 (9.52) 51.25 (12.61)
0.845
Digital WCST64 53.00 (11.09) 50.30 (9.53)
Effect Time Pressure and Complexity on WCST64 Performance
Table 9 shows the distribution and the effect of time, complexity, and age group on
digital WCST64 performance scores univariately. There was a significant effect in time on
digital WCST64 performance scores for stationary tasks (p<0.0001). Moreover, there were
significant effect in age group varied by time (interaction p = 0.010) (Table 10). The effect of
complexity on digital WCST64 performance scores was marginal differ by time (interaction p =
0.056) as shown in Table 10. The effect of complexity on digital WCST64 performance scores
was not significant by age (interaction p = 0.148) as shown in Table 11. Figure 17 depicts the
distribution of digital WCST64 performance score across conditions by age group.

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Table 9. The distribution and difference of digital WCST64 performance scores in time,
complexity, and age group at stationary
*Performance
score
Mean (SD)
β (95% CI) P
Time
Unlimited 51.76 (9.92) Ref
2 sec 40.43 (10.78) -11.34 (-13.95, -8.73) <0.0001
Complexity
4 options 47.23 (11.50) Ref
6 options 44.96 (12.03) -2.26 (-5.56, 1.03) 0.177
Age group
18-29 47.39 (10.89) Ref
64-75 44.80 (12.55) -2.59 (-7.35, 2.18) 0.279
*All performance scores are age and education level adjusted using error-t scores.
Table 10. The distribution and difference of digital WCST64 performance scores in age and
complexity by time
Time
Unlimited P
performance scores
2 sec
performance scores
Age group
18-29 51.38 (10.10) 43.40 (10.28) 0.010
64-75 52.15 (9.84) 37.45 (10.56)
Complexity
4 options 51.65 (10.30) 42.80 (11.03) 0.056
6 options 51.88 (9.65) 38.05 (10.10)
Table 11. The distribution and difference of digital WCST64 performance scores in complexity
by age
Age group
18-29 P
performance scores
64-75
performance scores
Complexity
4 options 49.73 (11.17) 44.73 (11.42) 0.148
6 options 45.05 (10.22) 44.88 (13.74)

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Figure 17. The distribution of digital WCST64 performance score across conditions by age
group
30
40
50
60
Digital WCST64 Performance Score
18-29 of age group
64-75 of age group
unlimited,
4 options
unlimited,
6 options
2 sec,
4 options
2 sec,
6 options

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Figure 18. The distribution of digital WCST64 performance score across conditions by age
group (boxplot)
Gait Performance and Cognitive Performance
For task comparison, the effect of walking was significant different on digital WCST64
performance scores compared to stationary (p=0.029). Time and complexity were also
significant on digital WCST64 performance scores univariately regardless of task type (p<0.0001
and 0.023 respectively) (Table 12). There were no significant differences on the digital WCST64
performance scores in time, complexity, or age by task (Table 13). There were no significant
differences in digital WCST64 performance scores in complexity by age (Table 14).
20
40
60
80
Digital WCST64 Performance Score
unlimited, 4 options unlimited, 6 options 2 sec, 4 options 2 sec, 6 options
18-29 64-75 18-29 64-75 18-29 64-75 18-29 64-75

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Table 12. The distribution and difference of digital WCST64 performance scores in task (dual
task walking versus single task standing), complexity, time, and age
*Performance scores
Mean (SD) β (95% CI) P
Task
(Single Task) Stationary 46.09 (11.79) Ref
(Dual Task) Walking 43.55 (13.07) -2.54 (-4.82, -0.26) 0.029
Time (DTW)
Unlimited 50.75 (10.76) Ref
2 sec 38.89 (11.24) -11.86 (-13.68, -10.03) <0.0001
Complexity (DTW)
4 options 46.14 (12.37) Ref
6 options 43.59 (12.51) -2.64 (-4.92, -0.36) 0.023
Age group (DTW)
18-29 46.44 (11.11) Ref
64-75 43.21 (13.57) -3.23 (-8.22, 1.75) 0.197
*All performance scores are age and education level adjusted using error-t scores.
Table 13. The distribution and difference of digital WCST64 performance scores in time,
complexity, and age by task
Task
single task (Stationary) P
performance score
Dual task (Walking)
Performance score
Time
Unlimited 51.76 (9.92) 49.74 (11.53) 0.573
2 sec 40.43 (10.78) 37.36 (11.55)
Complexity
4 options 47.23 (11.50) 45.06 (13.16) 0.741
6 options 44.96 (12.03) 42.04 (12.88)
Age group
18-29 47.39 (10.89) 45.49 (11.32) 0.579
64-75 44.80 (12.55) 41.61 (14.42)
Table 14. The distribution and difference of digital WCST64 performance scores in complexity
by age
Age group
18-29 P
performance score
64-75
performance score
Complexity
4 options 48.45 (11.05) 43.84 (13.23) 0.233
6 options 44.43 (10.88) 42.58 (13.96)

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Table 15 shows the distribution and effect in time, complexity, and age group on digital
WCST64 for walking. Complexity or age group was not significant different on digital WCST64
performance scores. There was a significant effect in time on digital WCST64 performance
scores for walking (p<0.0001). As shown in Table 16, complexity and age group were
significantly different by time on digital WCST64 performance scores during walking
(interaction p = 0.006 and <0.0001 respectively). The effect of complexity on the walking
digital WCST64 was not significant by age (interaction p = 0.841) as shown in Table 17. Figure
18 shows the distribution of digital WCST64 performance scores across conditions at walking by
age group.
Table 15. The distribution and difference of digital WCST64 performance scores in complexity,
time, and age for walking
*Performance score
Mean (SD) β (95% CI) P
Time
Unlimited 49.74 (11.53) Ref
2 sec 37.36 (11.55) -12.38 (-15.02, -9.73) <0.0001
Complexity
4 options 45.06 (13.16) Ref
6 options 42.04 (12.88) -3.03 (-6.45, 0.40) 0.083
Age group
18-29 45.49 (11.32) Ref
64-75 41.61 (14.42) -3.88 (-9.54, 1.79) 0.174
*All performance scores are age and education level adjusted using error-t scores.

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Table 16. The distribution and difference of digital WCST64 performance scores in complexity
and age by time
Time
Unlimited P
performance score
2 sec
performance score
Complexity
4 options 50.83 (11.09) 41.46 (11.85) 0.006
6 options 50.68 (10.50) 36.33 (10.03)
Age group
18-29 50.64 (10.27) 42.24 (10.36) <0.0001
64-75 50.86 (11.30) 35.55 (11.15)
Table 17. The distribution and difference of digital WCST64 performance scores in complexity
by age for walking
Age group
18-29 P
performance scores
64-75
performance scores
Complexity
4 options 47.18 (10.92) 42.95 (14.91) 0.841
6 options 43.80 (11.59) 40.28 (13.97)
Figure 19. The distribution of digital WCST64 performance scores across conditions at walking
by age group
20
30
40
50
60
Digital WCST64 Performance Score
18-29 of age group
64-75 of age group
unlimited,
4 options
unlimited,
6 options
2 sec,
4 options
2 sec,
6 options

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Figure 20. The distribution of digital WCST64 performance scores across conditions at walking
by age group (boxplot)
Table 18 describes the distribution of the mean of stride length mean and gait speed by
conditions. The mean of both stride length and gait speed are shown to be significantly different
across conditions (p = 0.0003 and p<0.0001 respectively). In multiple pairwise comparisons
(Table 19 and 20), conditions that compared to baseline, 2 sec, 6 options and 2 sec, 4 options
were shown to be significantly different in the mean of stride length and gait speed (both
unadjusted and adjusted p < 0.05). Then, conditions that compared to 2 sec, 6 options, unlimited,
6 options and unlimited, 4 options were significantly different in the mean of stride length and
gait speed (both unadjusted and adjusted p < 0.05). The condition comparisons to 2 sec, 4
options, both unlimited, 4 options and unlimited, 6 options were significant different in the mean
of gait speed (both unadjusted and adjusted p <0.05) but unlimited, 6 options was not significant
different in the mean of stride length (Table 18 and 19). Furthermore, the mean of stride length
was varied across condition by age group and the distribution is shown in Table 21 and Figure
20
40
60
80
Digital WCST64 Performance Score
unlimited, 4 options unlimited, 6 options 2 sec, 4 options 2 sec, 6 options
18-29 64-75 18-29 64-75 18-29 64-75 18-29 64-75

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19. However, the mean of gait speed variation across conditions was not influenced by age group
shown in Table 22 and Figure 20.
Table 18. Difference of the means of stride length and gait speed across conditions
Conditions Mean of stride
length (mm)
Mean (SD)
Difference
across
conditions
P
Mean of gait
speed (mm/ms)
Mean (SD)
Difference
across conditions
P
Baseline 1422.30 (223.15)
0.0003
1.15 (0.28)
<0.0001
2 sec, 6 options 1491.81
(215.95)
1.28 (0.29)
2 sec, 4 options 1465.65
(233.67)
1.24 (0.30)
unlimited, 6 options 1488.00
(222.15)
1.19 (0.28)
unlimited, 4 options 1423.81
(235.70)
1.15 (0.29)
Table 19. Multiple pairwise comparisons of conditions on the mean of stride length (mm)
Mean of stride length (mm)
P
Adjusted
P*
Mean
Difference
95% CI of
mean difference
2 sec, 6 options compared to
baseline 69.52 (33.65, 105.38) <0.0001 0.001
2 sec, 4 options compared to
baseline 43.35 (7.49, 79.22) 0.018 0.042
Unlimited, 6 options compared to
baseline 25.71 (-10.16, 61.57) 0.159 0.224
Unlimited, 4 options compared to
baseline 1.51 (-34.35, 37.37) 0.934 0.934
2 sec, 4 options compared to 2 sec,
6 options -26.16 (-62.03, 9.70) 0.152 0.224
Unlimited, 6 options compared to 2
sec, 6 options -43.81 (-79.67, -7.95) 0.017 0.042
Unlimited, 4 options compared to 2
sec, 6 options -68.00 (-103.87, -
32.14) <0.0001 0.001
Unlimited, 6 options compared to 2
sec, 4 options -17.65 (-53.51, 18.22) 0.333 0.368
Unlimited, 4 options compared to 2
sec, 4 options -41.84 (-77.70, -5.98) 0.023 0.042
Unlimited, 4 options compared to
unlimited, 6 options -24.19 (-60.06, 11.67) 0.185 0.228
* Adjusted p is Simes’ false discovery rate for multiple comparison

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Table 20. Multiple pairwise comparisons of conditions on the mean of gait speed (m/s)
Mean of gait speed (mm/ms)
P
Adjusted
P*
Mean
Difference
95% CI of
mean
difference
2 sec, 6 options compared to
baseline 0.13 (0.08, 0.17) <0.0001 <0.0001
2 sec, 4 options compared to
baseline 0.09 (0.04, 0.13) <0.0001 <0.0001
Unlimited, 6 options compared to
baseline 0.04 (-0.01, 0.08) 0.105 0.129
Unlimited, 4 options compared to
baseline 0.01 (-0.04, 0.05) 0.717 0.716
2 sec, 4 options compared to 2
sec, 6 options -0.04 (-0.08, 0.003) 0.070 0.098
Unlimited, 6 options compared to
2 sec, 6 options -0.10 (-0.13, -0.05) <0.0001 0.0001
Unlimited, 4 options compared to
2 sec, 6 options -0.12 (-0.16, -0.08) <0.0001 <0.0001
Unlimited, 6 options compared to
2 sec, 4 options -0.05 (-0.09, -0.01) 0.021 0.033
Unlimited, 4 options compared to
2 sec, 4 options -0.08 (-0.12, -0.04) <0.0001 0.001
Unlimited, 4 options compared to
unlimited, 6 options -0.03 (-0.07, 0.02) 0.207 0.228
* Adjusted p is Simes’ false discovery rate for multiple comparison
Table 21. The distribution and difference of the mean of stride length across conditions by age
group
Conditions Mean of stride length (mm) Difference across
condition by age
group
P
Age group 18-29
Mean (SD)
Age group 64-75
Mean (SD)
Baseline 1512.47 (231.14) 1332.12 (177.95)
0.049
2 sec, 6 options 1531.16 (225.28) 1452.46 (204.23)
2 sec, 4 options 1513.02 (242.24) 1418.28 (220.62)
unlimited, 6 options 1502.20 (232.81) 1393.81 (202.26)
unlimited, 4 options 1484.70 (210.29) 1362.92 (249.01)

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Figure 21. The distribution of the mean of stride length across conditions by age group
Figure 22. The distribution of the mean of stride length across conditions by age group (boxplot)
1200
1300
1400
1500
1600
1700
Mean of stride length (mm)
18-29 of age group
64-75 of age group
Baseline unlimited,
4 options
unlimited,
6 options
2 sec,
4 options
2 sec,
6 options
500
1,000
1,500
2,000
Mean of stride length (mm)
baseline
18-29 64-75 18-29 64-75 18-29 64-75 18-29 64-75 18-29 64-75
unlimited,
4 options
unlimited,
6 options
2 sec,
4 options
2 sec,
6 options

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Table 22. The distribution and difference of the mean of gait speed across conditions by age
group
Conditions Mean of gait speed (m/s) Difference across
condition by age
group
P
Age group 18-29
Mean (SD)
Age group 64-75
Mean (SD)
Baseline 1.16 (0.34) 1.13 (0.20)
0.463
2 sec, 6 options 1.27 (0.34) 1.28 (0.23)
2 sec, 4 options 1.21 (0.35) 1.26 (0.24)
unlimited, 6 options 1.18 (0.32) 1.19 (0.24)
unlimited, 4 options 1.16 (0.31) 1.16 (0.27)
Figure 23. The distribution of the mean of gait speed across conditions by age group
1
1.1
1.2
1.3
1.4
Mean of gait speed (mm/ms)
18-29 of age group
64-75 of age group
baseline unlimited,
4 options
unlimited,
6 options
2 sec,
4 options
2 sec,
6 options

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Figure 24. The distribution of the mean of gait speed across conditions by age group (boxplot)
The distribution of the mean of stride length and gait speed for each condition including
baseline are provided in Table 23. The mean of stride length and gait speed for each condition
was compared to the baseline, it appeared that 2 sec, 6 op and 2 sec, 4 op for both stride length
and gait speed means was significantly difference from the baseline (Table 23).
0
.5
1
1.5
2
Mean of gait speed (mm/ms)
baseline
18-29 64-75 18-29 64-75 18-29 64-75 18-29 64-75 18-29 64-75
unlimited,
4 options
unlimited,
6 options
2 sec,
4 options
2 sec,
6 options

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Table 23. The distribution and difference in the mean of stride length (mm) and gait speed (m/s)
from each condition from baseline
Conditions
Mean (SD)
Difference from
baseline
Mean (SD) P
Mean of stride length
(mm)
Baseline 1422.30 (223.15) Ref --
2 sec, 6 options 1491.81 (215.95) 69.52
(132.20)
0.002
2 sec, 4 options 1465.65 (233.67) 43.35
(122.86)
0.018
unlimited, 6 options 1488.00 (222.15) 25.71
(110.50)
0.159
unlimited, 4 options 1423.81 (235.70) 1.51 (137.23) 0.934
Mean of gait speed
(m/s)
Baseline 1.15 (0.28) Ref --
2 sec, 6 options 1.28 (0.29) 0.13 (0.12) <0.0001
2 sec, 4 options 1.24 (0.30) 0.09 (0.14) <0.0001
unlimited, 6 options 1.19 (0.28) 0.04 (0.14) 0.105
unlimited, 4 options 1.15 (0.29) 0.01 (0.16) 0.717
Figure 25, 26, 27, and 28 are scatter plots that show the correlation of percent change
between the digital WCST64 performance scores and the mean of stride length and gait speed for
each condition from baseline as well as by age group. There was a significant correlation
between percent change digital WCST64 performance scores and mean of stride length for
unlimited, 4 options walking from baseline, although the correlation was small (ρ = 0.33,
p=0.04). When stratified by age group, those who were 18-29 had significant correlation (ρ =
0.50, p=0.03) (Figure 6b). Besides that, in 18-29 age group also had significant correlation
between percent change digital WCST64 performance scores and mean of stride length for 2 sec,
6 options walking from baseline (ρ = 0.52, p=0.02). The percent change in the mean of gait

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85
speed with the percent change in digital WCST64 performance scores did not appear to be
significantly correlated (Figure 27 and 28).
Figure 25. The correlation of the percent change between the mean of stride length and digital
WCST performance score for each condition at walking from baseline
ρ = 0.16, p = 0.33
-200
0
200
400
600
% change digital WCST total errors
(2 sec, 6 walking from baseline)
-20 0 20 40 60
% change in the mean of stride length
(2 sec, 6 options from baseline)
ρ = 0.08, p = 0.61
-100
0
100
200
300
% change digital WCST total errors
(2 sec, 4 walking from baseline)
-10 0 10 20 30
% change in the mean of stride length
(2 sec, 4 options from baseline)
ρ = -0.01, p = 0.95
-50
0
50
100
150
% change digital WCST total errors
(unlimited, 6 walking from baseline)
-10 0 10 20 30
% change in the mean of stride length
(unlimited, 6 options from baseline)
ρ = 0.33, p = 0.04
-100
-50
0
50
100
% change digital WCST total errors
(unlimited, 4 walking from baseline)
-40 -20 0 20 40
% change in the mean of stride length
(unlimited, 4 options from baseline)

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Figure 26. The correlation of the percent change between the mean of stride length and digital
WCST performance score for each condition at walking from baseline by age group
Figure 27. The correlation of the percent change between mean of gait speed and digital Blue
Goji WCST64 performance score on different condition
ρ = 0.52, p = 0.02 ρ = -0.24, p = 0.31
0
200
400
600
0 20 40 60 0 20 40 60
18-29 64-75
% change digital WCST total errors
(2 sec, 6 walking from baseline)
% change in the mean of stride length
(2 sec, 6 options from baseline)
ρ = 0.34, p = 0.16
ρ = -0.21, p = 0.38
0
100
200
300
-10 0 10 20 30 -10 0 10 20 30
18-29 64-75
% change digital WCST total errors
(2 sec, 4 walking from baseline)
% change in the mean of stride length
(2 sec, 4 options from baseline)
ρ = -0.33, p = 0.17
ρ = 0.31, p = 0.19
-50
0
50
100
150
-10 0 10 20 30 -10 0 10 20 30
18-29 64-75
% change digital WCST total errors
(unlimited, 6 walking from baseline)
% change in the mean of stride length
(unlimited, 6 options from baseline)
ρ = 0.50, p = 0.03
ρ = 0.30, p = 0.19
-50
0
50
100
-40 -20 0 20 40 -40 -20 0 20 40
18-29 64-75
% change digital WCST total errors
(unlimited, 4 walking from baseline)
% change in the mean of stride length
(unlimited, 4 options from baseline)
ρ = 0.14, p = 0.38
-200
0
200
400
600
% change in digital WCST total errors
(2 sec, 6 walking from baseline)
-20 0 20 40 60
% change in the mean of gait speed
(2 sec, 6 options from baseline)
ρ = -0.08, p = 0.64
-100
0
100
200
300
% change in digital WCST total errors
2 sec, 4 walking from baseline
-20 0 20 40
% change in the mean of gait speed
(2 sec, 4 options from baseline)
ρ = 0.06, p = 0.71
-50
0
50
100
150
% change digital WCST total errors
unlimited, 6 walking from baseline
-20 0 20 40 60
% change in the mean of gait speed
(unlimited, 6 options from baseline)
ρ = 0.23, p = 0.15
-100
-50
0
50
100
% change digital WCST total errors
unlimited, 4 walking from baseline
-40 -20 0 20 40
% change in the mean of gait speed
(unlimited, 4 options from baseline)

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Figure 28. The correlation of the percent change between mean of gait speed and digital Blue
Goji WCST64 performance score on different condition
Effect Size Time Pressure and Complexity on WCST64 Performance
In this study, the equivalence limits for study aim 1 is between -2.62 and 0.42 with the
mean of paired difference of -1.10 and standard deviation of paired difference of 4.76 which has
narrower equivalence limits compared to equivalence limits of -2.00 and 2.00 with the mean
paired difference of 1.00 with standard deviation of paired difference of 2.50.
For study aim 2, the effect size for the difference of combinations of time constraints,
number of sorting options at stationary and walking on performance of digital WCST64 are 0.42
and 0.46. These effect sizes are smaller compared to the proposed effect size of 0.73 with a
sample size of 96 based on a single factor, repeated measure design with a sample of 96 adults,
measured at 7 times with different conditions.
ρ = 0.35, p = 0.14 ρ = -0.07, p = 0.77
0
200
400
600
-20 0 20 40 60 -20 0 20 40 60
18-29 64-75
% change in digital WCST total errors
(2 sec, 6 walking from baseline)
% change in the mean of gait speed
(2 sec, 6 options from baseline)
ρ = 0.06, p = 0.81 ρ = -0.21, p = 0.37
0
100
200
300
-20 0 20 40 -20 0 20 40
18-29 64-75
% change in digital WCST total errors
(2 sec, 4 walking from baseline)
% change in the mean of gait speed
(2 sec, 4 options from baseline)
ρ = -0.13, p = 0.61 ρ = 0.24, p = 0.31
-50
0
50
100
150
-20 0 20 40 60 -20 0 20 40 60
18-29 64-75
% change digital WCST total errors
(unlimited, 6 walking from baseline)
% change in the mean of gait speed
(unlimited, 6 options from baseline)
ρ = 0.22, p = 0.37 ρ = 0.35, p = 0.13
-50
0
50
100
-40 -20 0 20 40 -40 -20 0 20 40
18-29 64-75
% change digital WCST total errors
(unlimited, 4 walking from baseline)
% change in theam of gait speed
(unlimited, 4 options from baseline)

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88
There was no effect size comparison as study aim 3 is exploratory and no predetermined
assumption was specified between gait performance and cognitive performance at study design
level. A sample size of 193 adults would be able to detect a correlation of 0.20 under the null
correlation of 0 based on Pearson correlation.
DISCUSSION
The purpose of this study was to evaluate the equivalence between traditional and digital
delivery methods of the Wisconsin Card Sorting Test 64 (WCST64), as well as to examine the
effects of time pressure and complexity on WCST64 performance and the association between
gait performance and cognitive performance. The descriptive data showed that the 40
participants were equally distributed across age groups and had a slight majority of female
participants (57.5%). Most participants were black (42.5%) and white (32.5%), and the mean
years of school completed was 17.25 years.
The analysis of traditional versus digital WCST64 showed no significant difference in
mean performance scores between the two delivery methods. This suggests that our digital
WCST is a valid delivery method to provide the same outcomes as the traditional WCST64.
However, it is important to note that there was non-equivalence observed which could be due to
the small sample size, as the 95% confidence interval of the mean difference was wide.
The analysis of the effects of time pressure and complexity on stationary WCST64
performance revealed that there was a significant effect in time on digital WCST64 performance
(p<0.0001). Furthermore, there were significant effect in age group varied by time (interaction p
= 0.010) (Table 10).

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89
The mean of both stride length and gait speed are shown to be significantly different
across conditions (p = 0.0003 and p<0.0001 respectively). In multiple pairwise comparisons for
each walking digital WCST64 task when time was constrained compared to baseline walking,
there was significantly increased average stride length in participants. When comparing
conditions that compared unlimited time conditions to, 2 second time constraint conditions there
was significantly decreased mean stride length. Similarly, when the unlimited + 4 options
condition during walking was compared to the 2 second time constraint + 4 options there was
also significantly decreased stride length. These findings suggest that time constraints are related
to increased stride length during dual task walking digital WCST64. For gait speed, in multiple
pairwise comparisons for each walking digital WCST64 task when time was constrained
compared to baseline walking, there was significantly increased average gait speed in
participants. Similarly, when comparing all time constraint conditions to all unlimited time
conditions there was significantly increased gait speed. These findings suggest that time
constraints are related to increased gait speed during dual task walking digital WCST64. Based
on existing literature, we would expect for more challenging EF tasks to elicit a slower walking
speed, but we may have observed the opposite because the walking task instructed to participants
to maintain a low to moderate % of maximal heart rate reserve appeared to be slower than what
participants would have self-selected. Thus, error in gait speed was exhibited through an increase
in average speed. Older adults have significantly shorter stride length than younger adults across
all walking conditions. There was no difference between age groups for gait speed.
There was a small significant correlation between percent change digital WCST64
performance scores and mean of stride length for unlimited, 4 options walking from baseline (ρ =
0.33, p=0.04). When stratified by age group, younger showed a significant correlation (ρ = 0.50,

Manipulating Cognitive Intensity During Aerobic Exercise
90
p=0.03) while older adults did not. Younger adults also had significant correlation between
percent change digital WCST64 performance scores and mean of stride length for 2 sec, 6
options walking from baseline (ρ = 0.52, p=0.02). There were no significant correlations
between the percent change in the mean of gait speed and the percent change in digital WCST64
performance scores from baseline.
Limitations:
Our results must be considered in the context of limitations. Because this was a proof-ofconcept study for potential mechanisms, the design was determined based on feasibility and study
funding. Thus, one limitation of this study was small sample size which led to the study being
underpowered. A larger sample size for a future study may provide more significant findings
especially in the correlations between cognitive performance changes and gait changes. Another
limitation was the few conditions for each variable that was investigated (time constraint,
complexity) to save time by allowing participants to complete data collection in a single visit.
While this study reduced the time and costs associated with a larger and longer trial, the results are
still complementary to future examinations of efficacy where we would indeed use a larger sample
size and increase the number of conditions investigated.
Conclusion:
The effect sizes generated here will provide investigators with preliminary data to help
design and justify future clinical trials. The results of this study suggest that the Blue Goji digital
of WCST64 may a valid alternative to the traditional card-based delivery method to assess EF.
Our findings also suggest that we can indeed systematically manipulate the intensity of a

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cognitive EF task by altering aspects such as time pressure. Furthermore, this study supports that
age is an important factor to consider during aerobic + cognitive exercise prescription.
there was significant effect in age group varied by time. The cognitive intensity of the Blue Goji
digital of WCST64 can also be progressively loaded during dual task walking. Specifically,
during the DTW Blue Goji digital of WCST64, performance on the cognitive task was
significantly affected by time constraint. Further, time constraint had a significant interaction
effect on both age and complexity during DTW Blue Goji digital WCST64. Gait metrics such as
stride length and gait speed were also affected by changes in cognitive intensity. Specifically,
longer stride length was observed for more cognitively difficult conditions DTW Blue Goji
digital WCST64. We observed a significant interaction effect of age on these stride length
changes. There appeared to be a significant moderate correlation between changes in stride
length and changes in cognitive performance in some conditions of DTW Blue Goji digital
WCST64. The findings of this research give our researchers guidelines for informing how to
progressively load and personalize the cognitive intensity of an executive function task during
future aerobic + cognitive training interventions.

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CHAPTER V:
SUMMARY & CONCLUSIONS
This study has demonstrated that the intensity of an executive function cognitive task can
indeed be progressively loaded during aerobic activity by altering aspects such as time constraint
and complexity in healthy adults. These findings provide a basis for developing a schema to
individualize the cognitive aspect of aerobic + cognitive exercise interventions intended to
improve cognitive function. There are three additional important findings we report from this
study: 1) the Blue Goji digital WCST64 is a valid delivery method to provide the same outcomes
as the traditional WCST64, 2) gait metrics such as stride length and gait speed were affected by
changes in cognitive intensity, and 3) the effect of time constraint and complexity on performance
were dependent on age group.
Executive function (EF) is the domain of cognition that is the most consistently identified
domain of cognition to benefit from training. Because EF, commonly referred to as the reasoning
or problem-solving domain, coordinates with all other domains of cognition, it would be a
monumental victory to humankind to be able to optimize interventions that target this domain for
improvement. EF abilities are very vulnerable to attenuation in the normal aging process because
they largely occur in the prefrontal cortex and cerebellar regions which both decline in volume as
we age. This research shows us that we may be able to reliably create intervention tools that can
combat this decline that all aging humans eventually experience.
Although our sample size was not powered to test for statistically significant changes in
outcomes other than differences in performance outcomes based on delivery method of the
WCST64, it did provide a practical approach to explore how manipulating factors of the
cognitive task such as time constraints and task complexity affects cognitive intensity of the task.

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We found evidence that the Blue Goji digital WCST64 may provide a valid alternative to the
traditional card-based delivery method to assess EF. Our findings also suggest that we can
indeed systematically manipulate the intensity of a cognitive EF task by altering aspects such as
time constraint and complexity. This study also suggests that age influences the effect size of
these manipulations. Additionally, we observed changes in cognitive intensity of the EF task
that were exhibited through changes in gait. The association between gait performance and
cognitive performance was observed, with both stride length and gait speed.
The most novel aspect of this study was the exploration of creating the basis of a schema
to progressively load the cognitive component of an aerobic + cognitive task. In this proof-ofconcept clinical trial, we generated effect sizes of time pressure and complexity on WCST64
performance on cognitive performance in healthy adults which will be helpful in developing
such a schema. In line with robust preliminary information in the literature, the effect sizes we
generated here suggest that the intensity of a cognitive load can be affected by overloading
cognitive reserve through manipulations of task parameters such as time constraint and
complexity. Thus, by accomplishing this initial step of proving that cognitive intensity can be
manipulated in a systematic way during aerobic + cognitive interventions, we can now follow
proper exercise prescription rules to develop aerobic + cognitive interventions that target
improvement in EF in adults.

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Abstract (if available)

Abstract Alongside an increase in human lifespan over the last several decades, technology has encouraged an increasingly sedentary lifestyle. This has exacerbated the prevalence of cognitive decline associated with an aging population. Purpose: The purpose of this project is to determine if executive function can be progressively loaded using multimedia delivery, time constraint, and dual task environments. Methods: A total of 40 recruited participants were equally allocated into two groups according to age (18-29: young adult; 65-75: older adult). After a collection of baseline descriptive data and acquisition to the Wisconsin Card Sorting Task (WCST64), participants completed 7 trials of the WCST64 under conditions with manipulated variables (time constraint, complexity, dual tasking). A 10 Meter Walk test was collected both before and after intervention. Cognitive performance was measured by the number of errors in the WCST64; gait performance was measured by changes in average gait speed and stride length as compared to baseline. A paired t-test was used to assess the difference between the conventional and digital version of the WCST64; the effect of progressive load (i.e., time constraint, number of sorting option, etc.) on WCST64 score while stationery and walking was assessed using a mixed-effect linear model. Results: There was no significant difference between the traditional and digital delivery of the WCST64 (p=0.152), and this difference did not vary by age group (p=0.845). There was a significant difference in errors across all four conditions while stationary and while walking (both p<0.0001). Additionally, the conditional effect was significantly dependent on age group (p=0.008; p=0.047 respectfully). Stride length and gait speed were both significantly different across conditions (p=0.0003; p<0.0001 respectfully). Significance: These results support the development of progressively loading cognitive tasks during aerobic + cognitive exercises that may improve EF, particularly as a preventative intervention for age associated cognitive decline.

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Creator Jones, Malcolm J. (author)

Core Title Manipulating cognitive intensity during aerobic exercise: clinical proof of concept

School School of Dentistry

Degree Doctor of Philosophy

Degree Program Biokinesiology

Degree Conferral Date 2024-05

Publication Date 02/15/2024

Defense Date 05/02/2023

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag aerobic training,angiogenesis,cognitive training,dual-task training,executive function,exergaming,neurogenesis,OAI-PMH Harvest,physical activity,synaptogenesis,virtual reality (VR) interventions

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Advisor Schroeder, Todd E. (committee chair), Finley, James (committee member), Fisher, Beth (committee member), Pa, Judy (committee member), Sigward, Susan (committee member)

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