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Impact of enhanced efficacy expectation on motor learning in individuals with Parkinson’s disease
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Impact of enhanced efficacy expectation on motor learning in individuals with Parkinson’s disease
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Content
IMPACT OF ENHANCED EFFICACY EXPECTATION ON MOTOR LEARNING IN
INDIVIDUALS WITH PARKINSON’S DISEASE
By
Yu-Chen Chung
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)
August 2017
Copyright 2017 Yu-Chen Chung
ii
ACKNOWLEDGEMENT
This dissertation work could not be completed without many individuals. I am grateful and
fortunate to have the guidance, support and help from all those people during the past six years that makes
this journey possible and smooth.
I would like to express my deepest gratitude to my primary advisor, Dr. Beth Fisher, for being my
rock throughout the whole process. She always provides me guidance and support as much as she can
both professionally and personally. Her insights and wisdom have inspired and helped developed my
research. She is extremely patient and devoting countless hours with me in discussing ideas, figuring out
the study design, interpreting the results, practicing presentations, and editing manuscripts. She is a role
model, who I will always look up to. I am also indebted to Dr. Carolee Winstein, my co-advisor, for her
guidance and mentorship. Dr. Winstein taught me how to ask critical questions and constantly challenged
my logic from different perspectives. I learned a lot from her regarding the essence of science and how to
investigate questions in a thorough and systematic way. I am also very grateful to Dr. Rebecca
Lewthwaite, who leads me into the field of social, cognitive, affective, and motor neuroscience. I am
thankful to Dr. Lewthwaite for keeping me on the right track and providing excellent suggestions for the
dissertation work. She is always accessible and willing to help. I would like to thank Dr. James Finley for
brings new thoughts and ideas into our discussion and data analysis. I would also like to acknowledge Dr.
John Monterosso for spending time discussing with me while bringing in his valuable insights and
expertise from the psychology fields. Finally, I am truly indebted to and thankful to all the study
participants that I worked with during the past six years, especially Mrs. Jan Carter for her support and
help.
I also owe my deepest gratitude to Dr. James Gordon for the support from Division of
Biokinesiology and Physical Therapy at the University of Southern California and to Dr. Amir Seif-
Naraghi for the sponsor from Mobility Research. In addition, I would like to thank all the faculty and staff
from the Division of Biokinesiology and Physical Therapy. Especially, I would like to thank Dr. Kornelia
Kulig, Lydia Vazquez, Veronica Perez, Janet Stevenson, Matt Sandusky, David Donaldson, Chad Louie
iii
and Ramraj Singh for always providing the most immediate assistance and support. Their help and
support made the whole PhD process possible.
I am grateful and feel very fortune to be part of the Motor Behavior and Rehabilitation Laboratory
(MBNL) and Neuroplasticiy and Imaging Laboratory (NAIL). I would like to thank Dr. Shailesh Kantak,
Dr. Hui-Ting Goh, Dr. Hsiang-Han Huang, Dr. Shu-Ya Chen, Dr. Eric Wade, Dr. Sue Duff, Dr. Ya-Yun
(Alice) Lee, Dr. Sujin Kim and Narissa Casebeer for their help and support. I gained valuable life and
school experiences from the conversation and discussion with them. They are good mentors as well as
lifetime friends who I will always cherish. I am also grateful to have Dr. Jo Smith, Dr. Na-Hyeon
(Hannah) Ko, Matt Konersman, Clarisa Martinez, Dr. Yi-An Chen, Dorsa Beroukhim Kay, Bokkyu Kim,
Helen Bacon, Yi-Ling (Irene) Kuo, Alaa Albishi, Andrew Hooyman, Rini Varghese, Alexander Garbin,
Phan Chanthaphun and Liya Yin as colleagues, who make the lab a big warm family. In addition to
MBNL members, I would also like to thank friends and colleagues from the department – Dr. Christopher
Laine and Shin Yo – for giving me advises.
Most importantly, I would like to acknowledge my family for their enduring love, support, and
understanding. Their unlimited mental support and encouragement is the driving force for me to move
forward and pursue this degree.
iv
TABLE OF CONTENTS
LIST OF FIGURES ...................................................................................................................................... v
LIST OF TABLES ...................................................................................................................................... vii
ABSTRACT ............................................................................................................................................... viii
CHAPTER 1: Background and Overview .................................................................................................... 1
CHAPTER 2: Motor performance-capability discrepancy in Parkinson’s disease and potential
contribution of efficacy expectation: a focused review and perspective ...................................................... 5
CHAPTER 3 ............................................................................................................................................... 43
Experiment 1: The impact of enhanced efficacy expectation on motor performance and learning in PD .. 43
Introduction ............................................................................................................................................. 43
Methods .................................................................................................................................................. 45
Results ..................................................................................................................................................... 49
Discussion ............................................................................................................................................... 57
Experiment 2: The impact of state anxiety resulting from performance criteria on motor performance and
learning in PD ............................................................................................................................................. 60
Introduction ............................................................................................................................................. 60
Methods .................................................................................................................................................. 61
Results ..................................................................................................................................................... 64
Discussion ............................................................................................................................................... 69
Conclusions ............................................................................................................................................. 72
CHAPTER 4: Summary and General Discussion ....................................................................................... 73
Summary of Main Results ...................................................................................................................... 74
Impact of self-efficacy on motor performance and learning in PD ......................................................... 75
Impact of state anxiety on motor performance and learning in PD ........................................................ 75
Clinical Implications ............................................................................................................................... 76
Future directions ..................................................................................................................................... 76
REFERENCES ........................................................................................................................................... 78
v
LIST OF FIGURES
Figure 2.1 A hypothetical schematic diagram illustrating the internal and external factors
that may influence efficacy expectation, and the potential function of efficacy
expectation as a mediating factor between physical capability and motor
performance. Physical capability is defined as the individual's physical capacity
to engage in the chosen activity. In other words, what an individual can do.
Physical capability will clearly be altered by impairments and changes in body
structure (e.g. disease severity, loss of muscle strength and endurance). Motor
performance is defined as observable motor behavior, namely what an
individual actually does. By evaluating and comparing task demands with
physical capability, an individual estimates his/her capability to successfully
accomplish the task and forms expectations regarding his/her upcoming motor
performance. Within the constraint of what an individual is actually capable of,
high efficacy expectation is proposed to put individuals in a preparatory state
ready for the task, sets a higher task goal, increases positive affect related to
performance and leads to more optimal motor performance (Wulf &
Lewthwaite, 2016). Thus, efficacy expectation serves as a modulator to
determine how much an individual’s motor performance is optimized and
reflects true physical capability.
15
Figure 3.1 Study procedure. All participants practiced a complex balance task on the first
day. On the second day, the participants returned for a retention test (see
Methods section for detailed description). The participants in the Enhanced
Expectancy (EE) group received a verbal Enhanced Expectancy statement and
time-in-balance criteria (i.e. definitions of a “good” trial), while the participants
in the Control group received no statement nor performance criteria.
48
Figure 3.2 Changes in self-efficacy rating after the Enhanced Expectancy statement
between the Control and EE groups. Error bar indicates standard error.
50
Figure 3.3 Self-efficacy ratings at acquisition and retention in the Control and EE groups.
51
Figure 3.4 Time in balance at acquisition and retention in the Control and EE groups.
52
Figure 3.5 Mean power frequency at acquisition and retention in the Control and EE
groups.
53
Figure 3.6 Correlation between reported nervousness before the start of each trial at
acquisition and retention time in balance between the Control and EE groups.
53
Figure 3.7 Changes in self-efficacy rating after the Enhanced Expectancy statement
between the Control and EE-noD groups. Error bar indicates standard error.
65
Figure 3.8 Self-efficacy ratings at acquisition and retention in the Control and EE-noD
groups.
66
Figure 3.9 Time in balance at acquisition and retention in the Control and EE-noD groups.
67
vi
Figure 3.10 Mean power frequency at acquisition and retention in the Control and EE-noD
groups.
67
vii
LIST OF TABLES
Table 2.1 List of cueing studies that demonstrate the performance-capability discrepancy
28
Table 3.1 Demographics information in the Control and EE groups.
49
Table 3.2 Responses in the questionnaire completed at the end of acquisition and retention
in the Control and EE groups.
53
Table 3.3 Demographics information in the Control and EE-noD groups.
64
Table 3.4 Responses in the questionnaire completed at the end of acquisition and retention
in the Control and EE-noD groups.
68
viii
ABSTRACT
Efficacy expectation serves as an important factor that modifies motor performance and learning
in non-disabled adults. Little is known about whether efficacy expectation may also have an influence on
motor performance in individuals with Parkinson’s disease (PD). To answer the question, we investigated
whether efficacy expectation enhancement would benefit motor performance in PD. A motor learning
paradigm was utilized to determine the short-term (i.e. acquisition) and long-term (i.e. retention) impact
of increased efficacy expectation on motor performance when individuals with PD acquired a motor skill.
There was a trend that enhancing efficacy expectation improved motor learning in individuals with PD. In
addition, increased state anxiety may have a negative impact on motor performance and counteract the
positive influence of enhanced efficacy expectation. The findings provide evidence that a component of
that movement dysfunction may be related to efficacy expectation. Boosting efficacy expectation and/or
alleviating anxiety in people with PD may represent an effective strategy in improving motor behavior.
1
CHAPTER 1
Background and Overview
Parkinson’s disease (PD) is a neurodegenerative disease characterized by a number of motor and
non-motor impairments that lead to progressive decline in motor performance and function. Though PD is
generally considered a movement disorder, recent evidence has suggested that non-motor deficits may
contribute to degraded motor performance in PD (Almeida, Valenca, Negreiros, Pinto, & Oliveira-Filho,
2016; Bryant, Rintala, Hou, & Protas, 2014; Mak & Pang, 2008, 2009a, 2009b; Nemanich et al., 2013).
For example, cognitive dysfunction involving set-shifting and attentional control has been found to be
associated with movement slowness in performing a finger sequence task (Lee, 2013) and freezing of gait
(Peterson et al., 2015; Peterson et al., 2016). In addition, the contribution of psychological factors on
motor performance has also begun to be recognized. Increased fear of falling in both individuals who
have fallen as well as those who have not can induce a slower and more variable gait, as well as more
freezing episodes in individuals with PD (Caetano, Gobbi, Sánchez-Arias, Stella, & Gobbi, 2009; Doan et
al., 2010, 2013, Ehgoetz Martens, Ellard, & Almeida, 2014, 2015).
Along these lines, the findings of several studies implicate that motor performance in people with
PD may not always match the full extent of their capability (Canning, Ada, Johnson, & McWhirter, 2006;
Ghilardi et al., 2000; Mazzoni, Hristova, & Krakauer, 2007; Salimpour, Mari, & Shadmehr, 2015). One
well-known example is kinesia paradoxa, a phenomenon in which individuals with PD experience
difficulty with movements in daily activities but can perform the same movements quickly and smoothly
with external sensory cues (Majsak, Kaminski, Gentile, & Flanagan, 1998; Majsak, Kaminski, Gentile, &
Gordon, 2008). Kinesia paradoxa can also be observed when the consequence of movement is altered,
such as when someone yells ‘fire’(Montgomery, 2004). Another example of the mismatch between
performance and capability is the placebo effect. The placebo effect is evident in PD when a simple
increase in an individual’s expectation of therapeutic benefits of an intervention can alleviate clinical
motor symptoms (Lidstone et al., 2010). These findings suggest a mismatch between motor performance
2
and capability in PD, as well as a possibility that difference in an individual’s expectation may contribute
to this mismatch.
Efficacy expectation, defined as a belief or expectation in one's ability to produce actions thought
important for success in specific situations (Bandura, 1977), is an important factor that modulates motor
performance and learning in non-disabled adults (Moritz, Feltz, Fahrbach, & Mack, 2000; Wulf &
Lewthwaite, 2016). Decreased balance efficacy expectations in individuals with PD compared to non-
disabled adults has been demonstrated as an independent predictor of gait deficits and postural instability.
Therefore, reduced efficacy expectation in PD may impact motor performance regardless of motor
impairments and thus, may be a potential source of the observed performance-capability mismatch.
Additionally, people with PD appear to have greater decrements in motor performance when task
instructions rely on the person’s perception of their capability. One common example is when people with
PD are told ‘to move as fast as you can.’ It is speculated that task instructions implying an individual’s
capability (self-related instruction) may evoke low efficacy expectation and deterioration of motor
performance, leading to a performance-capability mismatch.
To examine the above hypothesis, we conducted a focused review of the literature to explore
whether self-related instruction may lead to further degradation of motor performance and the potential
contribution of efficacy expectation to the performance-capability discrepancy. The thorough review
including 20 years of motor performance studies in PD reveals a trend in which people with PD
performed as well as non-disabled adults when the task instructions did not invoke any perception of
capability, whereas when the instructions were self-related, people with PD performed poorly. The
hypothesis that is being put forward in this focused review is that the decrement of motor performance
with self-related instructions is through low efficacy expectation. Although an association between
efficacy expectation and motor performance in PD has been well-documented, no studies examine
whether a direct manipulation of efficacy expectation can influence motor performance to assess
causality. It has yet to be determined whether efficacy expectation itself can directly impact motor
performance and even motor learning in individuals with PD, or whether the impact of efficacy
3
expectation is mainly indirect through reducing activity level, for example. Namely, low efficacy
expectation over time might lead to decreasing activity level, de-conditioning, and/or lack of practice. De-
conditioning and/or lack of practice then, would lead to future decline in motor performance. If the
influence of efficacy expectation on motor performance is direct, it may result in a performance level that
is inferior to one’s capability. Individuals with PD may have inaccurately low appraisal of their own
physical capability, which may limit their goal setting, underestimate achievable performance level and
reduce effort. Inaccurately low efficacy expectation may accelerate individuals to enter the vicious cycle
between activity restriction, de-conditioning and eventually lead to an actual decline in capability.
Determining the direct influence of efficacy expectation is essential for developing novel treatment
strategies and optimizing preserved functional ability in PD.
Thus, the overall objective of this dissertation is to investigate the impact of efficacy expectation
on motor performance and learning in individuals with PD. In order to manipulate efficacy expectation,
we employ methods that have been shown to improve efficacy expectation and concomitantly enhance
motor performance and learning in non-disabled adults. For one, studies have shown that a verbal
statement designed to increase an individual’s expectations for future success can boost efficacy
expectation and facilitate motor performance as well as learning (Wulf, Chiviacowsky, & Lewthwaite,
2012; Wulf, Lewthwaite, & Hooyman, 2013). Another method that has been used in previous studies is
providing performance criteria for success. This dissertation employs a combined approach in efforts to
enhance efficacy expectation by utilizing a verbal enhanced expectancy statement and performance
criteria to investigate the impact of efficacy expectation on motor performance and learning in PD. This
dissertation consists of one specific aim. To test this aim, individuals with PD will be assigned to either
an enhanced expectancy group or control group. Both groups of participants will be asked to learn a
complex balance task.
Specific aim: to investigate the impact of an enhanced expectancy statement combined with
performance criteria on efficacy expectation, motor performance and learning in individuals with PD
4
Hypothesis 1: individuals with PD in the enhanced expectancy group will show an increase in
efficacy expectation, compared with their counterparts in the control group
Hypothesis 2: individuals with PD in the enhanced expectancy group will demonstrate improved
motor performance at acquisition and at retention, compared to those in the control group when practicing
a complex balance task
Overview
This dissertation is organized into four chapters and includes two serial studies in order to address
the specific aim. Chapter 2 is a focused review and perspective, which explores the idea of performance-
capability mismatch and potential contribution of efficacy expectation to this mismatch in individuals
with PD. In the first empirical study (Chapter 3, experiment 1), we compare the effects of a verbal
enhanced expectancy statement in combination with performance criteria on efficacy expectation and task
performance when two groups of individuals with PD practiced a novel complex balance task. To rule out
the potential confounding impact of increased state anxiety reported by the enhanced expectancy group,
the data from the control group in Experiment 1 was used and compared with a third group of participants
with PD, who received only a verbal enhanced expectancy statement during practice (Chapter 3,
experiment 2). The final chapter (Chapter 4) provides a summary of results and general discussion of the
findings. The limitations, clinical implications, and future research direction will also be addressed.
5
CHAPTER 2
Motor performance-capability discrepancy in Parkinson’s disease and potential contribution of
efficacy expectation: a focused review and perspective
Contents
1. Introduction
2. Behavioral evidence demonstrating performance-capability discrepancy in PD
2.1. Task instruction may modulate the effect of cueing in PD
2.2. Motivational incentives improve motor performance in PD
2.3. Differences in expectation influence motor performance
3. Potential contribution of efficacy expectation to movement performance
3.1. The performance-capability discrepancy may derive from a common mechanism: efficacy
expectation
3.2. Differences in efficacy expectation may contribute to the performance-capability discrepancy:
behavioral evidence assessing the role of efficacy expectation on motor performance in PD
4. Cognitive processes that modulate the effect of efficacy expectation on motor performance
4.1. Action-specific perception can be modified by efficacy expectation in non-disabled adults
4.2. The amount of attentional resource and attentional demand for motor tasks may be
influenced by efficacy expectation in non-disabled adults
4.3. Efficacy expectation is associated with dual-task interference of gait speed in PD
5. Neurophysiologic processes that modulate the effect of efficacy expectation on motor
performance
5.1. Expecting successful motor performance and feeling capable is rewarding
5.2. Changes in the reward pathway are associated with efficacy expectation in non-disabled
adults
5.2.1. Striatum
5.2.2. Ventromedial prefrontal cortex and orbitofrontal cortex
5.3. Activation in primary motor cortex may be modified by expectation in non-disabled adults
5.4. The reward pathway is relatively preserved in PD
5.5. Evidence of reward- and expectation-induced neural activity changes in PD
6. Conclusions and directions for future studies
6
Introduction
Parkinson’s disease (PD) is a neurodegenerative disease that leads to progressive decline in motor
function. Current rehabilitation approaches directed at improving motor function primarily focus on
resolving impairments via stretching, strengthening, repetitive practice of functional activities, and
exercises (Abbruzzese, Marchese, Avanzino, & Pelosin, 2016; Montgomery, 2004; Tomlinson et al.,
2012). However, recent evidence suggests that motor impairments may not be the only contributors to
degraded motor function and performance (Almeida et al., 2016; Bryant et al., 2014; Mak & Pang, 2008,
2009a, 2009b; Nemanich et al., 2013). Researchers have begun to reveal that non-motor deficits, such as
reduced flexibility to transfer learned motor skill to different environmental contexts, may as well
contribute to degraded motor performance (Lee et al., 2016). Additionally, the implication of several
studies (Canning et al., 2006; Ghilardi et al., 2000; Mazzoni et al., 2007; Salimpour et al., 2015) suggests
that there may be a mismatch between motor performance and physical capability in individuals with PD.
For example, the fact that people with PD experience difficulty with movements in daily activities but can
perform the same movements quickly and smoothly in the presence of external sensory cues, suggests that
the physical capability for task completion is not the problem (Carlsen, Almeida, & Franks, 2013; Majsak,
Kaminski, Gentile, & Flanagan, 1998; Majsak, Kaminski, Gentile, & Gordon, 2008; Wang et al., 2011).
A similar observation can be made when the consequence of movement is altered and an additional
incentive is given, such as when someone yells ‘fire’ (Bonanni et al., 2010; Montgomery, 2004). Kinesia
paradoxa is a well-recognized example of this performance-capability mismatch and is used to describe
changes in performance with either external cues or incentives (Schlesinger, Erikh, & Yarnitsky, 2007).
These performance inconsistencies are typically thought to result from performance enhancement with
external cueing and are usually not considered as a discrepancy between motor performance and physical
capability. In this paper, we would like to examine the later interpretation and explore the potential factors
that can explain the performance-capability discrepancy.
Three main hypotheses have been proposed to explain performance inconsistencies from the
perspective of performance enhancement with external cueing. One early hypothesis is that the excitatory
7
signals sent from the basal ganglia to cortical motor areas are diminished due to dopamine depletion
(Berardelli, Rothwell, Thompson, & Hallett, 2001; Carlsen et al., 2013; Parr-Brownlie & Hyland, 2005;
Pascual-Leone, Valls-Solé, Brasil-Neto, Cohen, & Hallett, 1994). The presence of sensory cues or urgent
conditions increases activity in the supplementary motor area, premotor and motor cortices (Jahanshahi et
al., 1995), suppresses the abnormal activity in the dopamine depleted basal ganglia (Sarma et al., 2012),
and thus leads to performance improvement. Another hypothesis proposes that sensory cues allow
individuals with PD to bypass deficient basal ganglia motor circuits and compensate with parietal and
cerebellar circuits (Ballanger et al., 2008). A third hypothesis suggests that sensory cues may help
individuals with PD use attentional strategies, focus on the details of movements (e.g. the distance
between lines implicitly dictates step length), and thereby enhance their motor performance (Kelly,
Eusterbrock, & Shumway-Cook, 2012; Peterson & Smulders, 2015).
One common theory across the three main hypotheses is that motor performance in individuals
with PD improves via compensating for impaired motor automaticity (Peterson & Smulders, 2015).
External sensory cues and additional incentives (e.g., urgent conditions) can help activate cortical motor
areas, by bypassing the deficient basal ganglia motor circuit. Hence, the performance improvement is
primarily assumed to be a result of utilizing compensatory strategies. One way to look at performance
enhancement with cueing/incentive is to re-examine another possibility: can performance improvement
that is observed when for example, someone yells ‘fire!’ possibly indicate that individuals with PD may
actually have some capability preserved to carry out movement automatically (Robottom et al., 2009)?
Can this incongruity be explained by an event that can suppress self-related rumination (Radel, Pelletier,
Pjevac, & Cheval, 2017)? Can the performance-capability discrepancy be the result of an individual’s
expectation of their own impaired capability? One factor that needs to be considered but has not yet been
extensively explored is the role of efficacy expectation and its impact on motor performance in
individuals with PD.
Efficacy expectation, defined as one’s expectation in one’s capability to successfully execute a
given behavior required to produce an outcome (Bandura, 1977), is an important motivational factor to
8
modulate motor performance in non-disabled adults (George, 1994; Hutchinson, Sherman, Martinovic, &
Tenenbaum, 2008; Saemi, Porter, Ghotbi-Varzaneh, Zarghami, & Maleki, 2012; Weinberg, Gould, &
Jackson, 1979; Wulf et al., 2012; Wulf & Lewthwaite, 2016). Non-disabled young adults with heightened
efficacy expectation generated a target force level for a longer time in an isometric handgrip task, and
showed enhanced accuracy in a ball-throwing task, than those with low efficacy expectation (Hutchinson
et al., 2008; Saemi et al., 2012). A similar relationship between efficacy expectation and motor
performance has been reported in individuals with PD. Self-confidence in one’s ability to maintain
steadiness without losing balance (i.e. balance efficacy expectation) has been demonstrated to be an
independent predictor of gait speed, postural sway and fall incidence (Adkin, Frank, & Jog, 2003; Mak &
Pang, 2008, 2009b; Nemanich et al., 2013). The findings raise a possibility that reduced efficacy
expectation in individuals with PD may not simply be the byproduct of motor impairments, but may itself
be a source of further performance degradation.
In agreement with the above proposition, increased expectation has been shown to enhance motor
performance in individuals with PD. The placebo effect is an evident phenomenon. An increase in an
individual’s expectation of therapeutic benefits of an intervention can elicit an improvement in clinical
motor scores in PD (Lidstone et al., 2010). Based on the converging evidence, it is likely that reduced
efficacy expectation may be one factor contributing to the performance-capability discrepancy in PD. To
make the argument more concrete, we can apply this reasoning to a reaching study, in which movement
speed of goal-directed reaching in people with PD was compared between a self-determined maximal
speed condition and a visually cued condition using a rapidly moving target. In the visually cued
condition only, in which the instruction was to grab a ball when it passed through a target zone,
individuals with PD were as fast as non-disabled adults. Though the augmented visual information was
interpreted as the main reason for reaching speed improvement in the visually cued condition, it was not
the only difference between the two conditions. The task instruction in the self-determined maximal speed
condition, which was “reach and grasp the target as fast as you can,” may have reduced an individual’s
expectation of their own capability and thus interfere with motor performance. That is, individuals with
9
PD may expect that they will not be able to move fast due to PD. Whereas in the visually-cued condition,
participants were instructed to grasp the ball as it rolled rapidly along an inclined ramp and came into a
target zone and thus paid more attention to the task goal rather than being constrained by what they
thought they were capable of. The reduction in efficacy expectation in response to the task instruction
may be one possible contributor to decreased reaching speed observed in the self-determined maximal
speed condition.
The main goals of this perspective are twofold: (1) to assemble the behavioral evidence
demonstrating the proposed performance-capability discrepancy in individuals with PD and (2) to discuss
the potential contribution of efficacy expectation to the performance-capability discrepancy. This focused
review begins with empirical evidence demonstrating a mismatch between motor performance and
physical capability in PD by reviewing studies comparing motor performance between individuals with
PD and non-disabled adults. Studies demonstrating a similar discrepancy in non-motor tasks are beyond
the scope of this review and are not included (e.g. the flanker task in Wylie et al., 2009). The possibility
of efficacy expectation as one contributor to the performance-capability discrepancy is explored by
examining the influence of the task instructions and conditions employed in the listed studies. In the
second section, we examine the current literature suggesting a possible mediating effect of efficacy
expectation on motor performance in PD. Next, we discuss the cognitive processes and neural substrates
that are associated with the modulating effect of efficacy expectation to explore the possible mechanisms.
In the end, directions for future studies are proposed. This focused review is the first step to explore the
potential influence of efficacy expectation on motor performance in studies that have attributed problems
faced by individuals with PD as exclusively the result of deficits in motor capability. Given that PD has
been primarily considered as a movement disorder, this perspective may point out a novel therapeutic
direction. By addressing efficacy expectation in people with PD, a potent and effective rehabilitation
strategy may be developed.
10
Behavioral evidence demonstrating performance-capability discrepancy in PD
We propose an idea that a discrepancy exists between physical capability and motor performance in
PD based on three bodies of literature. The first body of literature examines cueing by listing differences
in task instructions between the conditions with and without sensory cues. We investigate whether
differences in task instructions would influence the impact of cueing, to explore whether motor
performance in individuals with PD would be more susceptible to different task instructions than non-
disabled adults as a result of low efficacy expectation. Specifically, we assess whether individuals with
PD would have degraded motor performance especially when task instructions invoke the person’s
perception of their capability. It is speculated that task instructions implying an individual’s capability (i.e.
self-related instructions) may lower efficacy expectation and lead to deterioration of motor performance.
The second body of literature is to review the behavioral evidence that investigates performance changes
in response to motivational incentives in individuals with PD. The third subset of literature concerns the
evidence that investigates the placebo and nocebo effects in PD.
Task instruction may modulate the effect of cueing in PD
To assess whether performance improvement in response to cueing may be modulated by task
instructions, we search the literature to include the studies that investigated the impact of cueing in
individuals with PD using the Pubmed database. The search terms were “Parkinson’s disease” combined
with cueing (“cues”, “cue”, “cueing”, “moving target”, or “moving targets”). The initial search yielded
878 articles. The studies were selected based on the following criteria. First, cueing is defined as “cues
that provide external temporal (timing or rhythmical) or spatial (size or amplitude) stimuli associated with
initiation and ongoing facilitation of motor activity”(Lim et al., 2005). Studies that investigated the
impact of cues specific to action selection (e.g. stimuli that hint which button to press next in a choice-
reaction time task or a sequential movement task) were excluded, considering that stimuli related to action
selection did not fall within the above definition of cueing. Second, only the studies that specify the task
instructions in the methods section were included. Third, observational studies that investigated the
impact of cueing on motor performance were selected. Studies examining the impact of cueing in
11
combination with training, or the impact of cueing on non-motor performance were not included. Fourth,
only the studies including non-disabled adults as a control group were selected. Fifth, the studies needed
to assess motor performance both with and without cueing.
The total number of included studies is 34. Table 2.1 lists the difference in task instructions used in
previous cueing studies and summarizes the findings. We compared motor performance in individuals
with PD with that in non-disabled older adults. Across studies, in addition to the presence of external
sensory cues, a main difference noted between cued and non-cued conditions was task instruction. In the
conditions without cues, the task instruction is self-related and determined by an individual’s self-
determined maximal capability, whereas, the task instruction in the cued conditions is usually not related
to an individual’s perceived capability. For example, in Kritikos et al. (1995), participants were instructed
to press each button as quickly and as accurately as possible in the non-cued condition, which was
dependent on self-perceived capability (self-related instruction), whereas in the cueing condition, they
were instructed to keep in time with the metronome beat when pressing the buttons. It is interesting to
notice that there appears to be a trend whereby individuals with PD show a decrement in motor
performance compared with non-disabled older adults, when task instructions were self-related. In
contrast, when the task instructions were not self-related, performance deficiency in PD compared to that
in non-disabled older adults was less. Self-related task instructions may evoke a reduction in efficacy
expectation in individuals with PD, thereby interfering with motor performance and leading to further
performance deterioration. Additionally, two studies demonstrated that a time constraint alone can
improve maximal movement speed in individuals with PD (Distler, Schlachetzki, Kohl, Winkler, &
Schenk, 2016; Majsak et al., 2008). Individuals with PD showed a faster movement speed than their self-
determined maximal speed when there was a time limit to successfully reach for targets. The
improvement in response to the time constraint only appeared in individuals with PD. When non-disabled
older adults were similarly challenged by a shorter time constraint, no further increases in movement
speed were observed. The above finding indicates that individuals with PD can improve motor
performance without cueing (i.e. a visual stimulus of a moving target), suggesting that other factors
12
besides visual stimuli may contribute to the enhanced performance in PD. We suspect that task instruction
may modulate motor performance in individuals with PD through impacting their efficacy expectation.
The traditional view of PD is that it a movement disorder characterized by cardinal motor features.
Bradykinesia, defined as slowness of movement, is one of the cardinal motor symptoms. Consistent with
the thought of performance-capability discrepancy proposed in this review, recent studies have provided
support for a hypothesis that bradykinesia can be in part explained by other factors than a
neuropathological process (i.e., the disease itself causes slowness), or the assumption that slowness is a
compensatory strategy to improve accuracy. Two studies have suggested that bradykinesia may instead
represent an increased tendency to move at slow speeds as a result of increased sensitivity to effort
expenditure (Mazzoni et al., 2007; Salimpour et al., 2015). Participants with PD showed a similar speed-
accuracy relation as non-disabled older adults, but they needed more attempts in order to reach a criterion
number of successful reaches within a pre-determined reaching speed, especially when the pre-
determined speed was fast (Mazzoni et al., 2007). The result suggests that people with PD have the
capability to move fast, but tend not to do so. To support our argument, we list the behavioral evidence
showing that motor performance in PD can be enhanced in the absence of external sensory cues in the
next section.
Motivational incentives improve motor performance in PD
Providing motivational incentives to encourage faster movement, such as a monetary reward or
avoidance of a painful shock, has been demonstrated to shorten reaction time and movement time of
reaching in individuals with PD, both during on and off medication states (Kojovic et al., 2014; Kojovic,
Higgins, & Jahanshahi, 2016; McDonald et al., 2015; Shiner, Seymour, Symmonds, et al., 2012).
Likewise, manipulating emotional states can elicit improvement in gait initiation, as emotions serve as
motivational incentives and drive humans to approach pleasant stimuli or avoid unpleasant ones. Viewing
pleasant and threatening pictures before walking improved anticipatory postural adjustments and gait
initiation respectively in individuals with PD, to a similar degree as in non-disabled older adults (Naugle,
Hass, Bowers, & Janelle, 2012). These findings indicate that motivational and psychological states can
13
induce improvement in motor performance in individuals with PD. Additionally, two studies show that
merely anticipating that a target must be grabbed before it disappears or goes out of reach is sufficient to
elicit faster movement speed in individuals with PD (Distler et al., 2016; Majsak et al., 2008). It is clear
that the enhanced performance cannot be attributed to impairments in physical capability. Nor it can be
fully explained by the compensatory hypotheses, such as utilizing external cues to bypass the deficient
basal ganglia pathway, or utilizing attentional strategies. We speculate that these performance
improvements (or inconsistencies) may suggest that individuals with PD preserve some capability to carry
out movement automatically. Namely, in some conditions, motor performance can be optimized and fully
reflect physical capability, while in other conditions, performance is inferior to capability and a mismatch
is observed. We suspect that the performance-capability discrepancy may be induced and explained by a
difference in an individual’s belief in and expectations of their physical capability (i.e. efficacy
expectation). In the next section, this idea is explored by reviewing evidence that suggests the impact of
expectation on modulating motor performance in PD.
Differences in expectation influence motor performance
Studies examining the placebo and nocebo effect reveal a possible explanation for the performance-
capability discrepancy: that expectation may be one source of this performance-capability mismatch in
PD (de la Fuente-Fernández, 2002; Mestre, Shah, Marras, Tomlinson, & Lang, 2014). The placebo effect
is defined as an improvement of symptoms after the administration of a treatment which by itself has no
known biological effect but which the patient believes to be effective and powerful. The nocebo effect is
opposite to the placebo effect and manifests as a worsening of symptoms. The nocebo effect is induced by
verbally-suggested negative expectations that the modification of treatment will exacerbate symptoms
while the treatment in fact remains unaltered. Expectation has been recognized as one major mechanism
underlying both the placebo and nocebo effects (Lidstone, 2014). Expecting improvement via the placebo
effect has been shown to enhance motor performance and alleviate symptoms in individuals with PD, as
demonstrated by increased movement velocity (Pollo et al., 2002), decreased Unified Parkinson’s Disease
Rating Scale (UPDRS) motor score (Lidstone et al., 2010; Mercado et al., 2006), reduced resting tremor
14
(Keitel, Ferrea, Südmeyer, Schnitzler, & Wojtecki, 2013), and lessened rigidity (Benedetti et al., 2004).
Likewise, expecting worsening performance through the nocebo effect has a negative impact, such as is
seen with reduced movement velocity (Pollo et al., 2002), increased UPDRS motor score (Mercado et al.,
2006), and intensified resting tremor (Keitel et al., 2013). Given that physical capability remains unaltered
due to no actual change in medication state, these performance changes are primarily attributed to the
participants’ expectations. The fact that performance changes can occur almost immediately after the
placebo and nocebo are received (Pollo et al., 2002), demonstrates that expectation is a powerful
modulator of behavior. Even in the absence of a deliverable (i.e., a sugar pill), we suggest that task
instructions alone may influence an individual’s expectation; subsequently affect motor performance, and
thereby contribute to the performance-capability discrepancy. Namely, under some task instructions, an
individual’s motor performance matches his own capability, whereas under others, there is a mismatch
between performance and capability. If we take Majsak’s study (1998) as an example, the task instruction
“reach and grasp the ball as fast as you can” may induce a reduced efficacy expectation in individuals
with PD (i.e. “I am not able to move fast because of Parkinson’s disease"), lower their expectation
regarding the upcoming performance, and lead to movement speeds that are far less than what the
participants are actually capable of achieving. Whereas, the instruction for the other condition “reach and
grasp the moving ball as it passes through“ would not diminish participants’ efficacy expectation, and
thus the movement speed matches the maximal limit determined by their physical capability.
Potential contribution of efficacy expectation to movement performance
The performance-capability discrepancy may derive from a common mechanism: efficacy
expectation
In the previous section, we established an argument that task instruction and placebo-nocebo
effects may impact performance via expectation. We propose that the effect of these factors may
specifically reflect the influence of efficacy expectation. Efficacy expectation indicates an individual’s
belief in their own capability, and differs from placebo-nocebo effects in that placebo-induced expectation
15
comes from an individual’s belief in the effectiveness of a treatment external to the self. While both of
them can increase an individual’s expectation and belief in achieving better performance, a critical
distinction is the source of expectation (See Figure 2.1). Efficacy expectation is a situation-specific
construct and can be influenced by task demands (e.g., the type of task, task conditions, and instructions)
(Bandura, 1997). Among the studies listed in the previous section that demonstrate the performance-
capability discrepancy, the factors that are altered are task instruction, and participants’ expectation.
Despite not being even considered in previous research, efficacy expectation is the common denominator
affected by these factors and is a promising explanation for the observed performance-capability
discrepancy. To examine the above preposition, the behavioral evidence specifically assessing the
association between efficacy expectation and motor performance in individuals with PD will be reviewed
in the next section.
Figure 2.1 A hypothetical schematic diagram illustrating the internal and external factors that may influence efficacy
expectation, and the potential function of efficacy expectation as a mediating factor between physical capability and
motor performance. Physical capability is defined as the individual's physical capacity to engage in the chosen
activity. In other words, what an individual can do. Physical capability will clearly be altered by impairments and
changes in body structure (e.g. disease severity, loss of muscle strength and endurance). Motor performance is
defined as observable motor behavior, namely what an individual actually does. By evaluating and comparing task
demands with physical capability, an individual estimates his/her capability to successfully accomplish the task and
forms expectations regarding his/her upcoming motor performance. Within the constraint of what an individual is
actually capable of, high efficacy expectation is proposed to put individuals in a preparatory state ready for the task,
sets a higher task goal, increases positive affect related to performance and leads to more optimal motor
performance (Wulf & Lewthwaite, 2016). Thus, efficacy expectation serves as a modulator to determine how much
an individual’s motor performance is optimized and reflects true physical capability.
eva l ua t e a nd compa re
(Al m e i da e t a l .,
2016; Ma k & Pa ng,
2009b )
(Be n e d e tti e t a l ., 2004; K e i te l e t a l ., 201 3; L idsto n e , 2014;
Lidstone e t a l ., 2010; Me r ca do e t a l ., 2006; Me s t r e e t a l .,
2014; Pol l o e t a l ., 2 002)
Efficacy expect a tion
(Belief in one’s own
ca p abilit y)
Motor performance
Medication state
Placebo‐ and nocebo‐ induced expectation
(Belief in the effectiveness of treatment)
Task demands
Type of task
Task condition
Physical capability
(e.g. motor i mpairment,
disease progress)
Internal source
External source
(Adk in e t a l ., 2003; Brya n t e t a l ., 2014; Cur tze e t a l ., 2016; Joh n s on et
a l ., 2013; Le e e t a l ., 20 16; Ma k & Pa n g, 2008, 2009a ; N e m a nich e t a l .,
2013; O ’Con n e l l & Guido n , 2 016; Roche st e r e t a l ., 2008)
16
Differences in efficacy expectation may contribute to the performance-capability discrepancy:
behavioral evidence assessing the role of efficacy expectation on motor performance in PD
While the impact of specifically manipulating efficacy expectation itself on motor performance in
individuals with PD has not yet been studied, the association between motor performance and efficacy
expectation has been established (see Figure 1). Given that postural instability and gait deficits are
hallmark symptoms of PD, a majority of studies have examined the association of balance and gait
performance with balance efficacy expectation using a cross-sectional study design. Balance efficacy
expectation has been shown to independently correlate with postural stability (Adkin et al., 2003; Johnson
et al., 2013; Lee, Altmann, McFarland, & Hass, 2016), gait kinematics (Bryant et al., 2014; Curtze, Nutt,
Carlson-Kuhta, Mancini, & Horak, 2016; Mak & Pang, 2008; Nemanich et al., 2013; O’Connell &
Guidon, 2016; Rochester et al., 2008), turning velocity (Curtze et al., 2016), and fall history (Mak &
Pang, 2009a), even after disease severity and muscle strength are accounted for. Mak and colleagues
examined the contribution of gait and postural impairments to balance efficacy expectation and reported
that only 13.4% of the variance of balance efficacy expectation could be explained by the UPDRS posture
and gait scores (Mak, Pang, & Mok, 2012). This result importantly suggests that reduced balance efficacy
expectation in PD may not be fully explained by motor impairments. Further, Curtze et al. (2016)
demonstrated that individuals with PD evaluated their balance efficacy expectation predominantly based
on performance during an off-medication state (i.e. in their worst state), even though they also performed
in an on-medication state and in fact are medicated throughout the day (Curtze et al., 2016). The finding
implies that individuals with PD believe their true capability is without any help from medication.
While balance efficacy expectation reduces as physical capability in balance and gait declines,
low efficacy expectation may be more than a pure ramification of proficiency and can potentially have an
impact on motor performance. This presumption is supported by three longitudinal studies showing that
balance efficacy expectation can predict fall and recurrent fall incidences in a six-month to one-year
follow-up period (Almeida et al., 2016; Lindholm et al., 2015; Mak & Pang, 2009b). Although the studies
do not rule out worsening motor impairments across one year as a possible cause of falls, the findings
17
suggest that low balance efficacy expectation may interfere with balance and gait performance, and lead
to higher fall incidence. It is possible that reduced efficacy expectation can negatively influence balance
and gait performance by causing activity restriction, lack of practice, de-conditioning and further
functional decline. However, the possibility that efficacy expectation itself may directly influence balance
and gait performance in individuals with PD has not yet been considered. Although the modulating
impact of efficacy expectation on motor performance has been primarily demonstrated in non-disabled
adults (Pascua, Wulf, & Lewthwaite, 2015; Wulf, Chiviacowsky, & Cardozo, 2014; Wulf et al., 2012;
Wulf & Lewthwaite, 2016), we may be able to shed light on whether such causality exists in individuals
with PD from another line of research. From research assessing motor performance in response to
increasing fear of falling (Brown, Doan, Whishaw, & Suchowersky, 2007; Caetano et al., 2009; Ehgoetz
Martens et al., 2014, 2015; McAuley, Mihalko, & Rosengren, 1997; Pasman, Murnaghan, Bloem, &
Carpenter, 2011; Shaw, Stefanyk, Frank, Jog, & Adkin, 2012), it has been shown that fear of falling alone
resulted in poor motor performance in PD. One could imagine that increased fear of falling reduces
efficacy expectation, and through reduced efficacy expectation, gait performance and postural control
declines. Of the many referenced studies, only Pasman et al., (2011) measured balance efficacy
expectation. Elevating the platform height of a standing surface increased fear of falling and reduced
balance efficacy expectation. With foot position and base of support remaining the same, individuals with
PD increased the frequency of postural sway during quiet standing in response to increasing fear of
falling, in a similar way as non-disabled older adults (Pasman et al., 2011). Pasman (2011) suggested that
increased fear of falling could mimic a potential balance deficit (Pasman et al., 2011). The assumption is
consistent with the findings of other studies, which report slower and more variable gait, as well as more
freezing episodes when individuals with PD experience increased fear of falling (Caetano et al., 2009;
Doan et al., 2010, 2013, Ehgoetz Martens et al., 2014, 2015). These modifications in balance and gait
have been suggested to increase the risk of falling and lead to a vicious cycle between fear of falling and
risk of falling (Doan et al., 2010, 2013).
18
Cognitive processes that modulate the effect of efficacy expectation on motor performance
How may efficacy expectation modify motor performance? Are there changes in cognitive
processes that can explain and support the observed performance modifications? Evidence that reveals
changes in cognitive processes due to different levels of efficacy expectation in non-disabled adults are
reviewed in this section. This review will help elucidate how efficacy expectation may modulate motor
performance in individuals with PD. Due to the paucity of evidence in PD, most of the studies are derived
from research conducted in non-disabled participants. The evidence that have been reported in individuals
with PD is outlined separately at the end of the section.
Action-specific perception can be modified by efficacy expectation in non-disabled adults
It has been shown that perception is modified by efficacy expectation and thus may serve as one
potential process underlying its modulating impact on motor performance. How an individual perceives
the environment and task goal has been shown to be influenced by his or her ability to perform
successfully (Witt, 2011). Young adults who succeeded in putting a golf ball in a target circle perceived
the target circle to be bigger than those who were less successful (Witt, Linkenauger, Bakdash, & Proffitt,
2008). Children who hit a target with a ball more often judged the target to look larger than those who hit
the target less often (Cañal-Bruland & van derKamp, 2009). Similarly, boosting an individual’s efficacy
expectation can induce changes in perception. With increased efficacy expectation, young adults
maintained the same amount of isometric handgrip force for a longer time, and reported reduced
perceived exertion, than others whose efficacy expectation was not enhanced (Hutchinson et al., 2008).
Therefore, it is plausible that increased efficacy expectation may modify perception that is related to the
task goal and subsequently leads to more optimal motor performance. This notion is in agreement with
two studies that assessed motor performance changes when visual perception was manipulated with the
Ebbinghaus illusion (Chauvel, Wulf, & Maquestiaux, 2015; Witt, Linkenauger, & Proffitt, 2012).
Surrounding a target with small circles, which made the target appear bigger, improved accuracy in young
adults, compared to surrounding the same target with big circles when participants performed a golf-
putting task (Chauvel et al., 2015; Witt et al., 2012). In addition to improved putting accuracy, the
19
participants who practiced hitting the ball with the perceived bigger target also reported increased efficacy
expectation (Chauvel et al., 2015). Increased efficacy expectation predicted the improved putting
accuracy by explaining 20% of the variance. The findings indicate that increasing perceived target size
may enhance motor performance through boosting participants’ efficacy expectation (Witt et al., 2012).
The amount of attentional resource and attentional demand for motor tasks may be influenced
by efficacy expectation in non-disabled adults
Efficacy expectation may influence the attentional demand required for motor tasks, especially
when the tasks are related to balance and postural control. One study by Unemura et al. (2012) assessed
anticipatory postural control when older adults initiated gait as quickly as possible. The older adults who
reported fear of falling had a prolonged anticipatory postural adjustment during gait initiation compared
with those without fear of falling, under a dual-task condition when they were instructed to count
backward simultaneously (Uemura et al., 2012). Interestingly, the two groups of older adults ( with and
without fear of falling) showed no difference in gait initiation under a single-task condition or in measures
of physical function, such as the timed up & go and a functional reach test. The result suggests that
simply the fear of falling in conjunction with a secondary task, caused participants to re-allocate
attentional resources, such that a deficit in postural control occurred. In addition, the older adults who
reported fear of falling and reduced balance efficacy expectation showed greater difficulty in disengaging
their attention from fall-relevant stimuli, than their counterparts with high balance efficacy expectation
and no fear of falling (Brown, White, Doan, & deBruin, 2011). The older adults with fear of falling may
have less attentional resource to adjust posture and gait when encountering a potential threat to balance.
Hence fear of falling, which refers to reduced efficacy expectation that activities can be performed
without falling, may modify the amount of attentional resources for postural control and gait. A more
direct link between efficacy expectation and attentional demand of gait was demonstrated in a study
conducted by Gage et al (2013). Using a dual-task reaction time paradigm to assess the attentional
demand of gait, Gage and colleagues demonstrated that increased postural threat increased attentional
demands for locomotion in older adults (Gage, Sleik, Polych, McKenzie, & Brown, 2003). Non-disabled
20
older adults showed longer reaction time in response to auditory stimuli when walking on an elevated
platform, compared to reaction time when walking on the ground. Although efficacy expectation is not
measured in the above studies, it is plausible that fall-related fear may increase attentional demand
required for locomotion through reducing efficacy expectation.
Efficacy expectation is associated with dual-task interference of gait speed in individuals with
PD
Clearly, gait speed will decrease under a dual compared to a single task condition. However, for
individuals with PD, it has been reported that independently, balance efficacy expectation was related to
the dual-task impact on gait speed. This suggests a similar link between efficacy expectation and
attentional demand of gait as what is reported in non-disabled older adults. Balance efficacy expectation
was positively associated with gait speed in individuals with PD, especially under a dual-task condition in
which participants were instructed to hold a cup of water concurrently (O’Connell & Guidon, 2016;
Rochester et al., 2008). Balance efficacy expectation appeared to be the most explanatory factor for both
single- and dual-task walking speed, explaining 10% of the variance (Rochester et al., 2008). For
individuals with PD, gait performance in those with low balance efficacy expectation may be more
susceptible to additional attentional loading. However, the reported association does not allow researchers
to distinguish whether low efficacy expectation is the source of the problem or the result: such that the
increased attentional demand of a dual task leads to poorer performance, which then results in low
efficacy expectation.
Neurophysiologic processes that modulate the effect of efficacy expectation on motor performance
Expecting successful motor performance and feeling capable is rewarding
Feeling capable and competent has been proposed to be one of the essential psychological needs
for humans (Ryan & Deci, 2000). One way of feeling capable is through achieving successful motor
performance, such as fast and accurate movement. The achieved success is desirable and rewarding. In
lieu of actual performance, simply increasing an individual’s expectation of successful motor
21
performance implies a higher probability of future success. Thus, satisfying the innate need of feeling
capable and competent by enhancing an individual’s expectation and perceived competence could also be
rewarding. The thought that both actual and perceived competence is viewed as a rewarding experience
corresponds with neuroimaging findings. In general, studies have demonstrated that activation of the
‘reward circuit’ is higher as confidence (i.e. efficacy expectation) increases (Chua, Schacter, Rand-
Giovannetti, & Sperling, 2006; Daniel & Pollmann, 2012; Lebreton, Abitbol, Daunizeau, & Pessiglione,
2015; Schwarze, Bingel, Badre, & Sommer, 2013). In the following section, details related to the
potential neural substrates associated with increased confidence and expectation of successful
performance in animal models and non-disabled adults are discussed.
Changes in the reward pathway are associated with efficacy expectation in non-disabled adults
Striatum
In order to understand the role of the striatum in efficacy expectation, it is necessary to provide
further anatomical details. There are several functional circuits connecting the basal ganglia and the
cerebral cortex (Alexander, DeLong, & Strick, 1986). The striatum is the main area of the basal ganglia
which receives inputs from the cerebral cortex and projects to the globus pallidus. The globus pallidus is
the main output region of basal ganglia, which sends signals back to the thalamus and cerebral cortex.
The striatum can be divided into dorsal and ventral striatum. The dorsal striatum consists of the caudate
nucleus and the putamen, while the ventral striatum includes the nucleus accumbens. The motor circuit
connecting the putamen and primary motor cortex is thought to regulate the control of movement, while
the limbic circuit linking the ventral striatum to ventromedial prefrontal and orbitofrontal cortex is
assumed to be involved in the processing of reward and motivation. Due to the unique anatomical
locations and connections, the ventral striatum, putamen and globus pallidus have been proposed to be the
basal ganglia regions that are activated when behavior (i.e., movement) is modified by motivational state
(i.e., reward) (Liljeholm & O’Doherty, 2012a, 2012b; Pasquereau et al., 2007; Pessiglione et al., 2007).
For example, the neuronal firing rate of the putamen and globus pallidus during movement preparation
and initiation increased in monkeys when there was a higher probability of getting juice (Pasquereau et al.,
22
2007). A similar result was demonstrated in humans: higher activation of the ventral striatum and globus
pallidus was observed when the magnitude of monentary reward increased (Pessiglione et al., 2007).
Functional magnetic resonance imaging (fMRI) studies have demonstrated that there is an
association between activation in the ventral striatum and confidence level (Daniel & Pollmann, 2012;
Schwarze et al., 2013). In a study done by Schwarze and colleagues (Schwarze et al., 2013), non-disabled
young participants were presented with photos of unfamiliar outdoor scenes. The next day, participants
were presented with those scenes previously shown mixed with new scenes and asked to provide their
confidence in recognizing the old and new scenes. Activation of the ventral striatum was positively
correlated with reported confidence level: activation of the ventral striatum increased when participants
were more confident, and reduced when confidence level was lower. Importantly, the correlation between
confidence and the ventral striatum activation was independent of the correctness of responses, indicating
that activity in the ventral striatum could be modulated by perceived confidence alone. In addition to the
ventral striatum, the activation of the putamen has also been demonstrated to increase with higher
confidence level (Daniel & Pollmann, 2012). These results suggest that confidence may modulate our
behaviors and actions through mediating activity of the putamen (i.e. the striatum area predominantly
projecting to motor areas), in order to sustain rewarding experience (i.e., the feeling of competence) in the
future.
Ventromedial prefrontal cortex and Orbitofrontal cortex
Evidence from rodent studies have suggested that orbitofrontal cortex may not only represent the
value of rewards, but also be involved in estimating and computing the expected rewards (Takahashi et al.,
2011). Compared to sham-lesioned rats, rats with an orbitofrontal cortex lesion showed reduced striatal
activity in response to unexpected reward (Takahashi et al., 2013). It has been postulated that
orbitofrontal cortex sends information of expected reward to the ventral striatum. This information is
important as the difference between expected and obtained reward may serve as an error signal to
modulate behaviors and drive learning (Takahashi et al., 2013). In line with the presumption that higher
confidence may imply a higher probability of expected reward, the neuronal activity of orbitofrontal
23
cortex was shown to match a rats’ confidence about their decision to move in one direction or another to
obtain a food pellet (Kepecs, Uchida, Zariwala, & Mainen, 2008). Kepecs et al. (2008) also varied the
timing of reward delivery and found that rats modulated their behavior based on their confidence in their
decision to move left or right. Rats were more willing to wait for rewards when they were more confident
about their choices.
The findings from human fMRI studies suggest that in humans, confidence seems to be more
correlated with the activity of ventromedial prefrontal cortex than the activity of orbitofrontal cortex
(Chua et al., 2006; De Martino, Fleming, Garrett, & Dolan, 2013; Lebreton et al., 2015; White, Engen,
Sørensen, Overgaard, & Shergill, 2014). Higher confidence elicited greater activation of ventromedial
prefrontal cortex in non-disabled young adults (Chua et al., 2006; Lebreton et al., 2015; White et al.,
2014). Interestingly, high confidence seems to be associated with reduced activation of the orbitofronal
cortex (Beer & Hughes, 2010; Beer, Lombardo, & Bhanji, 2010), which is different from the findings in
animal studies. In one study conducted by Beer (Beer et al., 2010), non-disabled young participants were
asked to choose between two places in the United States that had a higher percentage of population below
poverty level, or choose between two places that had a higher average July temperature. Following the
response, the participants were asked to provide their confidence in their choice. The temperature
question was designed to evoke over-confidence whereas the response accuracy was comparable between
the poverty and temperature questions. Participants tended to assume that they were more successful at
reasoning the temperature questions because it was easier to decide based on the geographical locations.
The over-confidence level was determined by the difference between average confidence level and the
percentage of correct responses. Non-disabled young adults who were less over-confident (i.e., less
confidence) showed a greater activation of the orbitofrontal cortex than those whose confidence were
more influenced by the temperature question (Beer et al., 2010).
It has been suggested that ventromedial prefrontal cortex and orbitofrontal cortex have distinct
functions (Wallis, 2012). Orbitofrontal cortex is involved in assigning and updating the rewarding values
of external stimuli in the environment, while the ventromedial prefrontal cortex processes the value
24
associated with internal processes and social rewards, such as, love and trust (Wallis, 2012). Even though
ventromedial prefrontal cortex and orbitofrontal cortex both connect with the striatum, the putamen is
mainly linked to ventromedial prefrontal cortex (Ongur & Price, 2000). The finding implies that
ventromedial prefrontal cortex is the more likely prefrontal region underlying the impact of efficacy
expectation on motor performance, compared to orbitofrontal cortex. Future studies are required to
confirm the above speculation. Considering that efficacy expectation is a prospective construct, which
differs from the retrospective confidence assessed in the above neuroimaging studies, caution should be
taken when future studies test the above speculation.
Activation in primary motor cortex can be modified by expectation in non-disabled adults
Currently there is no evidence with regards to whether efficacy expectation would induce changes
in activity of motor cortex. The closest evidence that could be used to infer possible changes in motor
areas is derived from the placebo and nocebo effect literature. Two studies investigated the changes in
corticomotor excitability associated with the placebo and nocebo effect (Andani, Tinazzi, Corsi, & Fiorio,
2015; Fiorio, Andani, Marotta, Classen, & Tinazzi, 2014). When non-disabled young adults expected to
perform better in a maximal isometric force production task, they showed enhanced corticomotor
excitability as measured by increased motor evoked potential (MEP) amplitude and reduced cortical silent
period (CSP) duration with stimulation over the representational area of the motor cortex, compared to
those who expected no behavioral improvements (Fiorio et al., 2014). More importantly, the modulation
of corticomotor excitability was found only in the muscle involved in the task, suggesting that the effect
was muscle-specific, rather than a general energizing effect. A similar but slightly different finding was
reported when an individual’s expectation was manipulated in the opposite direction. Non-disabled young
adults who expected to generate a lower force showed decreased CSP duration with MEP amplitude
unchanged (Andani et al., 2015). One study investigating placebo-induced fatigue reduction with
electroencephalogram (EEG) showed that participants who expected a decrease in fatigue reported a
lower rate of perceived exertion than those who had no expectation (Piedimonte, Benedetti, & Carlino,
2015). Additionally, the EEG readiness potential amplitude before movement initiation increased with
25
fatigue level in the control group, but did not change in the expectation group. As the readiness potential
is tightly associated with the activity of supplementary motor area and primary motor cortex, the result
suggests that expectation may mediate the preparatory phase of movement and change the activation of
motor areas.
The reward pathway is relatively preserved in PD
Parkinson’s disease is a neurodegenerative disorder resulting from progressive dopamine
depletion in the basal ganglia. The depletion level follows a general pattern, which initiates from
dorsolateral area and progresses to ventromedial area when disease stage advances, leaving the
dorsolateral striatum markedly disrupted and the ventromedial striatum, a part of the reward circuit,
relatively spared (Kish, Shannak, & Hornykiewicz, 1988; Schonberg et al., 2010). Individuals with PD
were not as efficient in associating a reward-predicting cue with an actual reward compared to non-
disabled adults (Schott et al., 2007). However, they were able to adjust their task performance in response
to a reward similar to non-disabled older adults (Kojovic et al., 2014; McDonald et al., 2015; Ravizza,
Goudreau, Delgado, & Ruiz, 2012; Schott et al., 2007). These results demonstrate reduced reward
prediction ability in individuals with PD but no deficit associated with responding to a reward. A
compatible finding was also reported when the task goal was associated with social interaction.
Participants with PD modulated kinematics of reaching and grasping similar to non-disabled older adults
when the goal was to hand an object to another person compared to when the goal was to place the object
on a table (Straulino, Scaravilli, & Castiello, 2015). Hence, it is likely that efficacy expectation could
modulate behaviors and actions in individuals with PD through activating the relatively preserved reward
circuit.
Evidence of reward- and expectation-induced neural activity changes in PD
It has been shown that the activity of the striatum could be altered by reward in individuals with
PD (Kühn et al., 2008). Specifically, the provision of a reward strengthened the association between post-
movement basal ganglia activity and performance accuracy. This result indicates that the basal ganglia
activity involved in processing performance feedback can be modulated by reward in people with PD in
26
order to optimize performance in the next trial. The notion that basal ganglia activity in PD could be
modulated by reward or expected reward itself is in line with the placebo effect literature (Frisaldi,
Carlino, Lanotte, Lopiano, & Benedetti, 2014). A pure increase in people’s expectation of therapeutic
benefits of medication increased dopamine release in the putamen and ventral striatum (Lidstone et al.,
2010; Strafella, Ko, & Monchi, 2006). Additionally, excessive activation of the subthalamic nucleus was
decreased (Benedetti et al., 2004). Expectation can also influence corticomotor excitability in individuals
with PD (Lou et al., 2013). In general, individuals with PD exhibit higher corticomotor excitability as
measured by increased MEP amplitude, compared to non-disabled adults. The hyper-excitability is
suspected to be a compensatory mechanism for dopamine depletion (Cantello, Tarletti, & Civardi, 2002).
Lou and colleagues demonstrated that PD participants who were told that they would not receive
medication showed increased MEP amplitude (i.e. worsened hyper-excitability), while those who were
told there was a chance of receiving medication showed no MEP change (Lou et al., 2013). A recent
study showed that expectation of receiving medication enhanced activation of the ventromedial prefrontal
cortex that are related to expected value, and attenuated the activity of the ventral striatum in response to
prediction errors and rewards (Schmidt, Braun, Wager, & Shohamy, 2014).
Conclusions and directions for future studies
In this perspective paper, we reviewed behavioral evidence demonstrating a mismatch between
motor performance and capability, which may not be fully explained by previous proposed hypotheses.
We explored the evidence that difference in expectation for upcoming performance, which is largely
determined by efficacy expectation, may have an impact on motor performance in individuals with PD
and explain the observed performance-capability discrepancy. Although there was indirect evidence
suggesting that efficacy expectation itself may influence motor performance in PD, no study has been
conducted to test the above assumption. The first step to investigate the above assumption is to examine
whether a direct manipulation of efficacy expectation would impact motor performance in individuals
with PD. In addition, future studies will be needed to validate whether efficacy expectation may be the
27
possible factor explaining the modulating impact of self-related task instructions on the effect of cueing in
PD. Or other factors that are not considered before but now known to affect motor performance in PD,
such as attentional focus (Landers et al., 2005; Wulf et al., 2009), may explain the modulating impact of
task instructions. Finally, there appears to be a disconnection and knowledge gap between current
evidence with regards to potential underlying cognitive processes and neurophysiologic mechanisms
related to efficacy expectation. The reward and attentional pathways may function in concert and
influence each other (Ivanov et al., 2012; Robinson et al., 2012). The interaction and relative contribution
to motor performance in individuals with PD may require future research.
28
Table 2.1. List of cueing studies that demonstrate the performance-capability discrepancy
Authors Task Condition Task instruction Type of
cue
Other conditions Dependent variable Findings
(compared to non-disabled controls)
1 Freeman
et al.,
1993
Finger
tapping
produced
by wrist
flexion
and
extension
Non-cued “Subjects were instructed to
tap in rhythm with the cue
signal during the initial 10
second phase and then to
continue to tap at the same
rhythm during the remaining
20 second phase of the trial
when the cues were absent.”
(non self-related instruction)
None None mean frequencies
variability of tapping
frequency
Mean frequency: The mean tapping rates of the PD
group, across the whole range of signal each
frequencies, differed significantly in the presence
and absence of cues (p = 0 01, MANOVA) while
those of the control group did not.
Variability of tapping frequency: tapping
performance of the patients, at each signal
frequency, was more variable than that of the
controls (p=0.01).
Cued
“Subjects were instructed to
tap in rhythm with the
auditory "clicks".”(non self-
related instruction)
Auditory
cues
target
tapping
frequencies
: 1, 2, 3, 4,
5 Hz
2 Georgio
u et al.,
1993
Sequential
finger
tapping
task
(Subjects
were
instructed
to press
each
button “as
quickly
as
possible”.
)
(self-
related
instructio
n)
Non-cued Subjects were instructed to
reproduce the pathway 'as
best they could'.
(self-related instruction)
None Visual cue Down time
Movement time
Down time: PD patients were overall significantly
slower in initiating movements than controls. For
control subjects, non-contingent auditory cue provided
by the metronome significantly improved (26 ms) the
initiation of movements as compared with the
temporally contingent auditory cue conditions. For PD,
provision of the non-contingent auditory cue greatly
improved DT performance.
Cued 1.Auditory low (at button
release)
2.Auditory median (at button
press)
(For both of these auditory
conditions, subjects were
instructed to operate 'as best
they could')
(self-related instruction)
3.Auditory high (4.8 Hz)
(Subjects were to repeat the
same sequence without visual
cues, this time keeping in
time as best they could with
each beat of the metronome)
(self-related instruction)
Auditory
cue
Movement time: parkinsonian patients (401 ms) were
overall significantly slower in executing movements
than controls.
For PD, provision of both contingent and non-
contingent auditory cues greatly improved MT
performance
3 Kritikos
et al.,
1995
Sequential
finger
tapping
Non-cued “Subjects were instructed to
press each button as quickly
and as accurately as
None Presence and
absence of visual
cue
Down time
Movement time
Down time: no group difference
Movement time: PD patients were slower in movement
execution than the controls (p<0.01).
29
task possible”
(self-related instruction)
Cued “For the slowest and fastest
speeds (1.5 and 6.67 Hz) they
were instructed to press the
buttons of the pathway as
quickly as they could,
without making errors and
ignoring the metronome.”
(self-related instruction)
Auditory
cues
1.5 Hz
2.5 Hz
4.0 Hz
6.67 Hz
Only the 4 Hz metronome speed significantly
improved the performance of the PD patients,
compared with the condition without visual cue
(p<0.05).
The performance of the normal controls was
significantly faster than the PD patients in all
conditions, except the 2.5 Hz metronome condition,
where controls slowed, and the 4 Hz metronome
condition, in which the performance of the patients was
significantly speeded.
“For the 2.5 Hz and 4.0 Hz
speeds, they were instructed
to keep in time with the
metronome beat.”
(non self-related instruction)
4 Rickards
et al.,
1996
Visuo-
motor
tracking
tasks
Non-cued “The subject’s task was to
superimpose the movement
cursor on the target cursor as
the two cursors
synchronously moved across
the screen.”
(non self-related instruction)
1. Reduced
target
cursor
2. Reduced
movement
cursor
None tracking errors Temporary suppression of the target cursor had little
apparent effect on the accuracy of the control subject
whereas suppression of the movement cursor elicited a
more clear-cut reduction in accuracy. By contrast, the
most dramatic impairment of the patient’s tracking
performance is evident during target suppression.
Cued Visual
1. Target
cursor
2. Moveme
nt cursor
Under non-suppressed conditions the tracking accuracy
of the PD group was on average poorer than that of the
controls.
significant between-subject main effects were found
for group (PD or control, p = 0.007)
5 Burleigh
-Jacobs,
1997
Gait
initiation
Non-cued “Subjects were instructed to
take a forward step with the
left foot and follow through
with the right foot.”
(non self-related instruction)
None On and off
medication
(levodopa)
Force production
COP excursion
COM velocity
Step length
Duration of anticipatory
and push-off phase
During self-generated step initiation, the PD subjects
exhibit the appropriate posterior-lateral direction of
COP excursion, but the magnitude is reduced when PD
subjects are OFF.
The PD subjects OFF showed reduced vertical force
production compared with PD ON and compared with
the control subjects.
Cued “Subjects were instructed to
take a forward step with the
left foot as soon as they
perceived a brief, 4-ms
current pulse delivered to
the hand or the earlobe and to
continue the step through
with the right foot.”
(non self-related instruction)
cutaneous
stimulus
The cutaneous cue improved the anticipatory postural
adjustments for step initiation in the PD subjects OFF,
in a manner similar to levodopa administration. PD
subjects when OFF generated greater swing limb
vertical forces in the step-to-cue condition than in the
self-generated step condition. In contrast, control
subjects and PD subjects ON showed similar
performance in the two conditions. When a step was
initiated in response to the cue, the force production
was similar for PD subjects both OFF and ON and the
controls. The performance of the PD subjects when
OFF was not significantly different from either the
controls or the same subjects ON when the step was
30
initiated in response to the external cue
“Subjects were instructed to
take a forward step with the
left foot as soon as they
perceived the cutaneous cue
and to continue to step
through with the right foot.”
(non self-related instruction)
cutaneous
cue with
backward
surface
translation
The control subjects, but not the PD subjects,
responded to the perturbation with increased force,
COM velocity, and step length. Control subjects, but
not PD subjects, experienced increased magnitude of
ground reaction forces during anticipatory postural
adjustments and were therefore able to more rapidly
execute foot-off and increase step length when the
perturbation imposed a small, passive forward sway of
the body. The swing limb peak vertical force was
significantly less in the PD subjects OFF (p = 0.002)
and ON (p = 0.025) compared with the controls in the
step-to-perturbation condition.
6 Majsak
et al.,
1998
Reach and
grasp
Non-cued
(stationary
ball
condition)
“Reach and grasp the
stationary ball as fast as
possible”
(self-related instruction)
None None peak reaching velocity
time to peak velocity
movement time
spatial accuracy of
reaching
PD slower than controls
Cued
(moving
ball
condition)
“Reach and grasp the moving
ball as it passed through the
contact zone”
(non self-related instruction)
Visual
stimulus of
moving
target
PD similar to controls
7 Azulay
et al.,
1999
Gait
Off-med
Non-cued “Subjects were instructed to
walk at their natural speed,
looking straight ahead
without any specification
regarding foot positioning”
(self-related instruction)
None
(walks on
a uniformly
grey flat
surface)
Illumination:
normal lighting
stroboscopic
(electronic)
illumination at 3
Hz
velocity
stride length
cadence
double limb support
duration
During the control situation (normal light, normal
ground), the mean gait velocity of patients with
Parkinson's disease (0.76 ± 0.2 m/s) was slower than
that of controls (1.13 ± 0.2 m/s). The mean stride
length was shorter (925 ± 176 mm versus 1172 ± 193
mm, respectively); the cadence was reduced (99 ± 11.3
steps/min versus 116 ± 10.7, respectively); and the
relative double limb support duration was greater (13 ±
2.3% versus 9 ± 1.4%, respectively). These results
were obtained for patients and controls walking at their
preferred speed.
Cued Visual cue
(walks with
parallel
transverse
white
strips)
A significant effect of the stripes only in patients with
Parkinson's disease. The parkinsonian patients walked
significantly faster with stripes on the floor (0.82 ±
0.19 m/s) than without (0.76 ± 0.2 m/s), but only with
normal light. Despite the improvement of their gait due
to the stripes, Parkinson's disease patients remained
significantly less skillful than the healthy subjects.
8 Liu et
al., 1999
Visuo-
motor
tracking
tasks
Non-cued “The subject was instructed to
keep tracking the estimated
position of target and had
visual feedback of his or her
own movement from the
cursor which remained visible
throughout.”
“The subject was instructed to
track the continuously
displayed target without
1. Reduced
target
cursor
2. Reduced
movement
cursor
On and off
medication
tracking error
tracking velocity SD
PD patients had significantly larger errors in their
tracking than controls (P<0.000001), and there were
significant increases in the tracking error in both
patients and controls between tracking conditions with
and without visual cues (P<0.02).
However, there was no significant interaction, which
suggests that the increase in error was similar in the PD
patients and in the controls.
31
visual feedback of his or her
movement position.”
(non self-related instruction)
Cued “The subject was instructed to
make a wrist flexion
movement to keep
the cursor inside or as near to
the moving target as
possible”
(non self-related instruction)
Visual cue
Both cursor
and target
were
displayed
9 Lewis et
al., 2000
Gait
On-
medicatio
n
Non-cued “walk to the end of the
runway at your normal
speed”
(self-related instruction)
None None stride length
gait velocity
cadence
CV of velocity, cadence,
stride length
In baseline conditions, average gait velocity was
significantly slower in the Parkinson's disease patients
than in the control group (p = 0.005).
Compared with similar aged control subjects, the
patients demonstrated, on average, a 24% reduction in
gait velocity and a 23% reduction in stride length,
while cadence was relatively comparable.
Cued `walk to the end of the
runway by stepping over the
lines'
`step up to the line as you
walk along the runway'
(non self-related instruction)
Visual cue
step length
(SL) marker
subject-
mounted
light device
(SMLD)
With the addition of visual SL markers, patient stride
length increased to 1.34 ± 0.09 m, which was not
significantly different from baseline control stride
length (P = 0.14). A reduction in cadence that
approached significance was evident (P = 0.008), but
overall gait velocity was increased from baseline
conditions (P = 0.003). These two parameters were
also not significantly different from baseline control
results (cadence: P = 0.02; velocity: P = 0.03).
Similar results were seen for the patients when using
the SMLD
1
0
Almeida
et al.,
2000
In-phase
and off-
phase
movement
Non-cued “Subjects were told that the
tone of the metronome would
stop halfway into the trial,
and that they were to do their
best to maintain the pace set
previously by the metronome
until the end of the trial”
(self-related instruction)
None None Relative phase accuracy
(absolute mean error)
stability (standard
deviation)
Participants with PD experienced difficulty in
producing the anti-phase task but not the in-phase
coordination task relative to controls, although both
groups coordinated anti-phase less accurately than in-
phase movements overall. Individuals with PD were
more variable during the anti-phase pattern, but not the
in-phase pattern, when compared with controls.
Cued “Participants were instructed
verbally to coordinate
simultaneous and continuous
displacements of the two
linear sliding devices toward
and away from the midline of
the body, and in a plane
parallel to the body with the
goal of replicating the
prescribed line on the
Auditory
cue
(0.75, 1.25,
and 1.75
Hz)
Neither of these performance measures indicated any
influence of the external timing device. Both absolute
mean error and standard deviation of relative phase
failed to reveal any significant main effects or
interactions associated with the availability of tone
32
computer screen”
(non self-related instruction)
1
1
Kelly et
al., 2002
Reach Non-cued Subjects were instructed to
reach out and touch the target
“whenever you are ready”
1. Accurate reach
(subjects were instructed to
move “as accurately as
possible”)
2. Fast reach
(subjects were instructed to
move “as quickly as
possible” and touch
“anywhere” on the ball
target)
(self-related instruction)
None On and off
medication
(levodopa)
Peak wrist velocity
movement time
endpoint error
path ratio
PD never reached as quickly as age-matched controls
in any conditions
Cued
Subjects were instructed to
reach out and touch the
target upon illumination of
a red light emitting diode
embedded in the target
1. Accurate reach
(subjects were instructed to
move “as accurately as
possible”)
2. Fast reach move
(subjects were instructed to
move “as quickly as
possible” and touch
“anywhere” on the ball
target)
(self-related instruction)
Red light
emitting
diode
embedded
in the target
Subjects with PD reached more quickly in the fast
versus accurate condition, and on versus off levodopa.
There were significant main effects of speed and drug
on peak wrist velocity and movement time, but no
main effect of cue. We found that the cue increased
reaching velocity greatest when subjects were off
medication.
When this subject reached without a cue, levodopa
increased peak wrist velocity and decreased movement
time. When the cue was present, levodopa caused a
minimal change in peak wrist velocity and movement
time.
1
2
Dibble et
al., 2004
Gait
initiation
on-med
Non-cued “Begin the walking process
as quickly as possible and
continue walking to the end
of the walkway.”
(self-related instruction)
None None Temporal variables
(reaction time latency
[RT], DLS duration and
SLS duration)
COP variables (lateral
COP displacement [L-
COP], posterior COP
displacement [P-COP],
and COP velocity)
sacral displacement, step
length, sacral velocity,
and swing limb velocity
In all conditions, person's with PD reacted more slowly
and moved less far than did the matched elders.
Cued The participant was instructed
to “begin the walking process
as quickly as possible and
continue walking to the end
of the walkway” after they
received the movement
initiation stimulus.
(self-related instruction)
a single
auditory
cue (SA)
repetitive
auditory
cues (RA)
repetitive
cutaneous
cues (RC)
Relative to conditions with NCs, sensory cueing
resulted in decreased double limb support (DLS), and
increased COP displacement and velocity in both
groups. However, in both groups, displacements and
velocities of the swing limb and sacrum during the
sensory-cued conditions were less than those during
the NC condition.
These results suggest that when movement speed is a
33
primary goal, sensory cues may interfere with swing
limb and body movement outcomes during the gait
initiation task in both person's with PD and healthy
elders.
1
3
Ma et
al., 2004
Sequential
movement
(reaching
for a pen,
bringing
the pen
to the
paper, and
writing
down the
phrase
“Call 1-
800-
6060.”)
Non-cued “The participants were told to
start the movement when they
were ready”
(non self-related instruction)
None None Movement time
Peak velocity
Peak to average velocity
ratio
Number of movement
units
Movement of the patients was characterized by shorter
movement time, higher peak velocity, lower
PV/AV (in the first step only), and lower movement
variability in the signal-present condition than in the
signal-absent condition.
Cued “The participants were
instructed to start the
movement when they heard a
bell ring”
(non self-related instruction)
Auditory
cue
PD group had higher peak velocity in the signal-
present condition than in the signal-absent condition,
whereas the movement of the control group had similar
amplitude of peak velocity under both conditions.
1
4
Mak et
al., 2004
Sit to
stand
(STS)
On-med
Non-cued “Participants were instructed
to carry out the STS
movement at a natural
speed”. A request was made
to stand up when ready, and
no cue to commence was
given.
(self-related instruction)
None None hip flexion & ankle
dorsiflexion torques
hip extension, knee
extension, and ankle
dorsiflexion torques
horizontal and vertical
velocities
movement time
When patients were asked to stand up at a natural
speed under self-initiated conditions, there were
reduced peak hip flexion and ankle dorsiflexion
torques, and prolonged time-to-peak hip extension,
knee extension, and ankle dorsiflexion torques from
STS onset, reduced peak horizontal and vertical
velocities, as well as a prolonged movement time when
compared with those of controls.
PD slower than Control
Cued “Participants were requested
to look at the light and listen
to the verbal command. On
the command
“get ready, stand up,”
pressure on the trigger button
turned on the circular light,
and the request was given to
carry out the STS
movement as soon as
possible.”
(self-related instruction)
audiovisual
cue
When AV cues were given to controls, there was no
change in their STS performance except for a small but
significant increase of 4% in the peak knee extension
torque
PD similar with Control
1
5
Rocheste
r et al.,
2005
A home-
based
functional
task
On-med
Non-cued “They were asked to walk at
their preferred speed while
concentrating on both tasks”
(walking and holding a tray)
(self-related instruction)
None None Walking speed
mean step length
step frequency
PD subjects walked significantly slower than the
control group During the first trial, PD subjects walked
with a mean speed of .70m/s, which was .25m/s slower
than control subjects, representing a difference of
26.5%. There was no significant change in walking
speed using auditory or visual cues and no significant
difference between cue type for either group.
Cued “Subjects were asked to
synchronize each step with
either the auditory tone or the
Auditory
cue
Neither group showed significant change in walking
speed with auditory or visual cues compared with no
cues in the first dual-motor task.
34
flash of light.”
(non self-related instruction)
Frequency of cueing was
determined by measuring the
time taken to walk 10 steps at
the subject’s preferred
walking speed.
Visual cue
Use of auditory cues facilitated a significant increase in
mean step length during a dual-motor task that was
also substantial from a clinical perspective,
representing a difference of 19%. There was also a
trend toward an increase in walking speed during the
dual-motor task with auditory cues. Visual rhythmic
cues also resulted in a trend toward improved walking
speed during a dual-motor task.
1
6
Galletly
et al.,
2005
Gait Non-cued The gait only task involved
walking 10 m at a
comfortable pace
(self-related instruction)
None Concurrent task (
calculation,
language, or motor
task)
When performing
the concurrent
tasks, subjects
were instructed to
concentrate on
both the gait and
the added task at
the same time
stride length,
velocity
cadence
stride length: 92.47%
velocity: 89.76%
cadence: 98.10%
Cued Subjects were asked to walk
along the path stepping over
the white lines
(non self-related instruction)
Visual stride length: 97%
velocity: 92.94%
cadence:95.54%
1
7
Willems,
2006
Gait
On-med
Non-cued Subjects were asked to walk
8m along the walkway at
preferred speed.
(self-related instruction)
None PD (freezer and
non-freezer)
stride length
mean step frequency
walking speed
double support duration
When PD subjects walked at preferred walking speed,
they walked slower and with shorter strides than
controls. In addition, step frequency was significantly
reduced, whereas double support time was significantly
increased compared to the control group.
Cued “Subjects were instructed to
synchronize their foot contact
with the metronome beat.”
(non self-related instruction)
Auditory
metronome
1. 20%
slower
2. 10%
slower
3. Preferred
4. 10%
faster
5. 20%
faster
In all cueing conditions PD subjects walked
significantly slower and used shorter strides than
controls. The step frequency remained lower than
controls in the cueing conditions but the difference was
not significant in the +10% condition. The double
support time was significantly longer for
PD than for controls in all conditions.
When comparing the non-cued walking with the cueing
condition at baseline, a significant increase of step
frequency was observed for the PD group, as well as
for the control group. Stride length did not change
across conditions for control subjects. PD subjects
showed a significantly larger stride length in the -10%
condition compared with the baseline condition and
compared with the -20% condition.
1
8
Willems
et al,
2007
Turning Non-cued “Walk towards the obstacle,
make a left turn around it and
return to your starting
position. Please walk at your
normal, comfortable speed.”
None PD (freezer and
non-freezer)
turn time
shape of the turning-arc
number of steps to turn
step length, width, and
In contrast to controls, PwPD used a wider turning-arc
and took smaller, narrower steps. In addition, they
demonstrated a higher Coefficient of Variation (CV) of
step duration (6.92%) compared to controls (4.88%).
35
(self-related instruction) duration for the left and
right leg separately
Cued “Walk towards the obstacle,
make a left turn around it and
return to your starting
position. Try to synchronize
every foot-contact with the
beat of the metronome.
Follow the cues while
walking and turning.”
(non self-related instruction)
Auditory
cues a
metronome
at a rhythm
equaling
the
subject’s
comfortable
step
frequency
Auditory cues reduced the CV of step duration in
PwPD (both freezers and non-freezers) during turning
(from 6.92 to 6.00%).
1
9
Baker et
al., 2008
Gait Non-cued ‘walk at your own
comfortable pace’
(self-related instruction)
None Single and dual
task (walking and
carrying a tray
with 2 cups of
water)
coefficient of variability
(CV) for step time and
double limb support time
Single task: Step time variability was significantly
higher in PD subjects than the control group. DLS time
variability was also significantly higher in PD subjects
Dual task: Step time variability was significantly
higher in the PD group compared to controls but no
significant difference was seen in DLS time
Auditory
cue
‘as you walk try to step your
feet in time to the beat’
(non self-related instruction)
rhythmical
auditory
cue at 90%
preferred
stepping
frequency
Single-task
A significant interaction between cue type x subject
type was found for step time variability. Variability
showed a trend of reducing with all cue types in PD
subjects and increasing in control subjects.
In PD subjects further analysis showed the reduction in
step time variability was significant with the
AUD+ATT cue type by 32% compared to baseline
values. While cues tended to increase step time
variability in controls, this was not significant for any
cue type.
A significant main effect of cue type and subject type
was seen for DLS time variability but no interaction
effects were found. Further analysis showed that in PD
subjects there were significant reductions in DLS time
variability with the ATT and AUD+ATT cue types
compared to baseline. There were no significant
changes in DLS time variability in the control group
with cues.
Dual-tasking
A significant main effect of cue type was found for
step time variability in the dual task condition but no
significant effect of subject type or interaction effects.
All cue types tended to reduce variability compared to
baseline in the PD group and this was significant with
the AUD+ATT cue. No significant changes were seen
in the control group with cues.
Double limb support time variability showed a
significant effect of cue type but no significant effect
Attentional
cue
‘as you walk try to take big
steps’
(non self-related instruction)
Instruction
to focus on
‘walking
with big
steps’
Combined
auditory
and
attentional
cues
‘take a big step in time to the
beat’
(non self-related instruction)
External
rhythmical
auditory
cue at 90%
preferred
stepping
frequency,
associated
with
‘taking a
big step’
36
of subject type or interaction effects
2
0
Baker et
al., 2007
Gait Non-cued Not specified None Single and dual
task (walking and
carrying a tray
with 2 cups of
water)
Walking speed
step frequency
step amplitude
dual-task interference
effect
PD group walking slower with smaller steps and a
reduced step frequency across all conditions. No
interaction effect of group and cue. Although PD
walked away more slowly and with smaller steps, they
responded in a similar to cue types and conditions.
Auditory
cue
“As you walk try to step your
feet in time to the beat”
(non self-related instruction)
Rhythmic
sound (90%
preferred
cadence)
The auditory cue reduced speed during single-task gait
but this change was not significant. In the dual-task
condition, the auditory cue type increased speed by
about 2%, which was not significant. Although the
auditory cue caused a small increase in step amplitude,
it was not significant in both single and dual task
conditions.
Attentional
cue
“As you walk try to take big
steps”
(non self-related instruction)
Instruction
to focus on
‘walking
with big
steps’
Walking speed was normalized to the level of the
control group at baseline in the dual-task condition
only
Combined
auditory
and
attentional
cues
“Take a big step in time to the
beat”
(non self-related instruction)
External
rhythmical
auditory
cue set 90%
preferred
stepping
frequency,
associated
with
‘taking a
big step’
Walking speed was normalized to the level of the
control group at baseline in the dual-task condition
only
2
1
Majsak
et al.,
2008
Reach and
grasp
Non-cued
(stationary
ball
condition)
“Reach as fast as possible to
grasp a ball placed within the
contact zone”
(self-related instruction)
None None reaction time
movement time
acceleration time
peak reaching velocity
PD slower than controls in movement time,
acceleration time, and peak velocity
Cued
(moving
ball
condition)
“Reach and grasp a moving
ball from within the contact
zone”
(non self-related instruction)
visual
stimulus of
moving
target
PD similar to controls
Non-cued
(drop ball
condition)
“Reach as fast as possible to
grasp a ball placed within the
contact zone”
(self-related instruction)
None PD slower than controls only in acceleration time and
peak velocity
2
2
Lebold
et al.,
2010
walked
through a
narrowed
doorway
on-med
Non-cued
Not-specified
“participant walked normally
across the GAITRite carpet
through the narrowed
doorway”
(self-related instruction)
None PD (freezer and
non-freezer)
mean step length
gait velocity
base of support (cm)
step time
double support time
step to step variability
Velocity: The PD FOG group walked significantly
slower (76.49 ± 25.87 cm/s) as compared to the PD
non-FOG group (93.04 ± 12.42 cm/s) and the Control
group (105.56± 13.15 cm/s). The difference between
PD non-FOG group and the Control group was not
statistically significant.
37
Step length: Post hoc analysis confirmed that PD FOG
(50.95 ± 11.09 cm) took significantly shorter steps
than both PD Non-FOG (61.27 ± 5.28 cm) and
Controls (67.18 ± 5.53 cm).
Cued Ground lines
“participant walked across the
GAITRite carpet while
making consecutive heel
contacts on ground lines
provided by a black overlay
placed on top of the entire”
(non self-related instruction)
Visual
transverse
line
In contrast to the baseline condition (Narrow), PD
FOG significantly decreased their velocity in the laser
condition, but did not significantly alter velocity with
ground lines.
In contrast to the baseline (Narrow), PD non-FOG did
not increase velocity in the ground line condition
(105.59 ± 18.67 cm/s and 99.36 ± 12.89 cm/s).
However, similar to the PD FOG group, the PD non-
FOG group experienced a significant decrease in
velocity in the laser condition (74.16 ± 20.68 cm/s).
Healthy control participants displayed the same pattern
as observed with PD Non-FOG, as baseline velocity
(119.02 ± 11.96 cm/s) was significantly faster than
observed when using the laser (86.86 ± 22.98 cm/s)
and not significantly different from the ground lines
condition (110.79 ± 12.83 cm/s).
PD FOG group were able to improve and normalize
only some of the characteristics of gait (i.e., step
length, variability etc.) to that seen in non-FOG
individuals with PD and healthy control participants
when ground lines were provided.
Laser condition
“The participant walked
across the GAITRite carpet
while making consecutive
heel contacts on lines on the
ground provided by a
motorized laser line device,
through the narrow doorway.”
“The individual was
instructed to touch their toe
to the laser line when it was
at its furthest point.”
(non self-related instruction)
Optic flow
2
3
Lohnes
et al.,
2011
Gait
On-med
Non-cued
Not-specified None Single and dual
task (word
generation task)
gait velocity
stride length
cadence
PD similar to age-matched controls
Cued “Subjects were asked to
synchronize each step with
the auditory tones”
(non self-related instruction)
rhythmic
auditory
cue at 10%
below and
10% above
gait velocity increased for age-matched controls with
COMB+10, and for PD with ATT, COMB-10, and
COMB+10.
Stride length increased above baseline for all three
groups with ATT and COMB+10, and for young
controls and PD with COMB-10
Dual task gait velocity increased for young and age-
matched controls with COMB+10. Stride length during
dual task walking increased for young controls, age-
matched controls, and PD with ATT and COMB+10,
and for young controls and age-matched controls with
COMB-10.
“think about taking large
strides”
(non self-related instruction)
Attentional
cue
‘take long
strides’
Not-specified A
combinatio
n of AUD
and ATT
2
4
Lebold
et al.,
Gait
Non-cued
“participants were required to
walk at a self-selected pace
None None gait velocity Overall, individuals with PD were found to walk with a
decreased step length, velocity, and cadence when
38
2011 On-med down the length of the carpet”
(self-related instruction)
cadence
mean step length
double support time
step-to-step variability
compared to the HC group.
Cued “participants were instructed
to touch each line with their
heel consecutively as they
proceeded through the trial”
(non self-related instruction)
lines on the
ground
Despite bradykinesia (i.e. slowness) being a principal
impairment of gait in PD, our data revealed that neither
ground lines nor laser cues resulted in an amelioration
of gait velocity.
Individuals with PD took significantly shorter steps at
baseline (54.79 ± 11.35 cm) compared to the HC group
(65.58 ± 8.38 cm). Both the ground lines (66.76 ± 2.2
cm) as well as the laser device (63.28 ± 10.85 cm)
were effective in significantly improving the step
length of individuals with PD above baseline levels.
The Ground Line condition was the only condition in
which step length was normalized in the PD group to a
level comparable to that of the HC group (69.68 ± 4.78
cm).
The Laser condition resulted in a step length in the PD
group comparable to that observed in the HC group in
the Baseline condition. HC participants increased their
step length beyond their own baseline with use of the
laser device (76.24 ± 10.42 cm), leading to a
significant improvement over the PD group in the
Laser condition.
In the Laser Dark condition, the PD group
demonstrated a significantly shorter step length (59.02
± 13.21 cm) compared to the HC group (71.24 ± 8.06
cm, p < 0.001). These findings were PD specific, since
no gait parameters were influenced in healthy
participants in the same Laser Dark condition.
The PD group experienced a significantly greater
within-trial step length variability (3.74 ± 1.64 cm)
compared to the HC group (1.66 ± 0.56 cm). There was
no significant difference between conditions in either
group.
Not-specified
“reverse optic flow was
provided by a laser line
projector set at 1 cycle per
second, as this was an
appropriate speed to be able
to follow the laser as
accurately as possible”
laser lines
(reverse
optic flow)
Not-specified laser lines
in a dark
environmen
t (removing
vision of
the legs)
2
5
Anzak et
al., 2011
Grip force
task
Non-cued
“to grip a force dynamometer
as quickly and strongly as
possible in response to a
visual cue”
(self-related instruction)
None On and off
medication
peak force
peak yank
time to peak force
time to peak yank
better baseline performance in healthy controls,
as demonstrated by the main effects for Group in peak
yank (114.2 ± 7.3 kg ⁄ s in patients with PD and 162.6
± 9.7 kg ⁄s in healthy controls) and time to peak yank
(178 ± 13 ms in patients with PD and 117 ± 6 ms in
controls) and a trend towards an effect for Group in
peak force (16.5 ± 0.6 kg in patients with PD and
20.7 ± 1.0 kg in healthy controls).
Cued loud (96
dB)
auditory
Patients showed improvements in the peak rate of force
development and the magnitude of force developed
when loud auditory stimuli accompanied visual cues.
39
stimulus Equally, they showed improvements in the times taken
to reach the peak rate of force development and their
maximal force. The paradoxical facilitatory effect of
sound was similar whether patients were off or on their
usual antiparkinsonian medication, and could be
reproduced in age-matched healthy controls.
2
6
Lee et
al., 2012
Gait
Off med
Non-cued
Not-specified None PD (freezer and
non-freezer)
Velocity, cadence, stride
length, step length, and
double- and single-limb
standing time.
Maximum pelvic tilt, hip
flexion, knee flexion, and
ankle dorsiflexion in the
sagittal plane
PDF group showed significantly decreased velocity
and stride length and significantly elevated cadence,
total step number, total walk time, and freezing
number, compared with the other two groups.
The PDF group had a significantly increased peak
extent of pelvic tilt and hip flexion and significantly
decreased degree of knee flexion and ankle
dorsiflexion.
The differences were not significant in PDNF and NV
on the baseline evaluation.
Cued All subjects were instructed
to walk at a step length as
indicated by the white stripes
(non self-related instruction)
Visual In contrast with what was observed in the PDF group,
visual cues significantly decreased velocity in both the
PDNF and NV groups, relative to baseline. Visual cues
significantly increased the extent of pelvic tilt and
ankle dorsiflexion in the PDNF group and decreased
knee flexion and increased ankle dorsiflexion in the
NV group.
Auditory cues minimally affected visuospatial
parameters but significantly increased knee flexion and
ankle dorsiflexion compared with baseline values in
the PDNF group. No significant effect of auditory cue
was observed in the NV group
The subjects were instructed
to synchronize their steps
with metronome beats.
(non self-related instruction)
Auditory
2
7
Fernánd
ez-Del-
Olmo et
al., 2012
Step
initiation
Non-cued
“They were instructed to
perform a rapid initiation of
gait with their right leg in
response to a visual
imperative stimulus (IS) and
keep walking until they
completed three consecutive
steps.”
(non self-related instruction)
None None onset latency and duration
of SOL, TA, and RF
EMG bursts and of soleus
inhibition (SOLin)
Although the SOL onset latency was significantly
faster for the SAS condition than for the LAS and IS
conditions in the control group, differences were not
significant for the PD group between SAS and IS
conditions or between SAS and LAS conditions.
TAL-SOL time difference in patients was significantly
longer than in control subjects for all conditions. In the
PD group, the TA-SOL time difference was
significantly longer in the SAS condition than in the IS
condition, whereas no significant differences were
found between these conditions in the control group.
Onset of toe-off was significantly slower in PD patients
than in controls. Both groups showed a significant, but
mild, shortening of onset of toe-off in the LAS
condition, in comparison with the IS condition. In the
SAS condition, only the control group demonstrated a
further decrease in onset toe-off, in comparison with
the LAS condition. In PD patients, the onset toe-off
Cued auditory
stimulus
1. non-
startle:
low (80
dB)
2. startle:
high
(130 dB)
40
was significantly faster in the SAS condition than in
the IS condition.
2
8
Delval et
al., 2014
Gait
initiation
Non-cued
(self-
triggered
condition)
“Start as fast as possible at
the moment of your choice
and with the starting foot of
your choice”
(self-related instruction)
None On and off
medication
PD (freezer and
non-freezer)
Backward and lateral
COP shift
Reaction time
duration of APA
amplitude of APA
numbers of APA
speed, length and duration
of first step
PD was slower than controls in first-step speed, first-
step length, APA duration
Cued
(auditory
cueing
condition)
“Taking a step forward (and
continuing to walk onwards)
as soon as possible after
hearing the sound signal”
(self-related instruction)
Auditory
sound of
movement
initiation
In PD FOG patients, cueing was found to reduce
abnormal postural preparation of the first step.
Unsurprisingly, abnormal step preparation was less
common in non-freezers and HCs, with, respectively
no APAs in 14% and 5% of the trials in the ST
condition and in 0% and 2% of the trials in the cued
condition. Step time and length were lower in the cued
condition but there was no group by condition
interaction. Cueing did not have a significant
effect on first-step speed in either group
2
9
Rabin et
al., 2015
Gait
(Participa
nts were
instructed
to “walk
as well as
possible”
in all
conditions
)
(self-
related
instructio
n)
Non-cued
Participants were instructed to
“walk as well as possible” in
all conditions
(self-related instruction)
None On and off
medication
speed
stride length
stride time
double-support time
medial-lateral trunk
position
Participants with PD walked slower, with shorter,
briefer strides, longer double support, and less medial-
lateral movement than healthy controls.
Compared with healthy controls across conditions,
participants with PD walked 68% as quickly, with 82%
of the stride length, 81% of the stride time , 160% of
the double support, and 71% of the medial-lateral
excursion
Cued Haptic cue
Banister
( touching
the moving
handrail
without
movement)
Dopaminergic medication effects had no significant
interaction with the handrail condition. Lateral contact
reduced medial-lateral excursion during gait, but the
banister degraded walking, while the moving handrail
increased speed and stride length.
Within participants with PD, lateral manual contact
reduced the body’s mean medial-lateral position
unassisted amplitude by 30% with the banister and by
37% with the moving handrail. The moving handrail
increased speed by 16% and stride length by 10%,
while the banister reduced speed by 11%, and stride
length by 8%. Double-support time with the banister
was 35% longer than walking unassisted, and 46%
longer than with the moving handrail. Stride time with
the banister was 3% longer than unassisted and 10%
longer than with the moving handrail. The moving
handrail did not change timing parameters significantly
from walking unassisted.
Subjects were instructed to
maintain manual contact with
the same place on the moving
surface
Haptic
speed cues
from the
moving
handrail
(touching
the handrail
moving at a
self-
selected
speed)
41
3
0
Brodie et
al., 2015
Gait Cued
cued a
usual
cadence
and cued
with
varying
paired-step
asymmetrie
s of
1. −10%
2. −5%
3. 0%
4. +5%
5. +10%
”Please walk normally while
stepping in time with the
beats.”
(non self-related instruction)
rhythmic
auditory
cueing at
habitual
cadence
with
different
a/symmetri
es
on and off
medication
gait speed
stride length
stride duration
double-support duration
medial-lateral excursion
For the PD and HAM groups, auditory cues at
preferred symmetry changed gait steadiness but not
walking speed, step length, cadence or symmetry.
When cued at preferred symmetry, PD had increased
head smoothness, and reduced step time variability,
and showed a trend for reduced PCI.
When walks were cued at habitual cadence and
preferred asymmetry, gait smoothness and step time
variability improved for PD, deteriorated for HAM
controls.
Non-cued
(unassisted)
Participants were instructed
to: “Please walk at your
usual speed”.
(self-related instruction)
None walked slower, with shorter, briefer strides, longer
double support, and less medial-lateral movement than
healthy controls
Using haptic speed cues from the moving handrail,
people with PD walked faster by spontaneously (i.e.,
without specific instruction) increasing stride length
without altering cadence; banisters slowed gait.
No condition x group interaction has been reported
3
1
Nackaert
s et al.,
2016
Writing
(“participa
nts were
instructed
to produce
natural
and fluent
loops and
write at a
comforta
ble
speed”)
(self-
related
instructio
n)
Non-cued
Not specified
(“the target zones were
initially presented to indicate
the requested amplitude but
disappeared after 2 s”)
None 0.6 cm and 1.0 cm writing amplitude
variability of amplitude,
and speed
Patients with PD wrote significantly smaller overall
than healthy controls
Cued Participants were instructed to
start writing within the start
circle and write the loops
from the bottom of the blue
target zone to the top of the
yellow target zone and, then,
returning to the bottom of the
blue zone
visual cues
(target
lines)
Healthy controls wrote significantly faster without cues
in the 0.6-cm condition, whereas in the 1.0-cm
condition, they wrote faster in the presence of cues. No
significant differences were found for patients with PD.
3
2
Wang et
al., 2011
Reach and
grasp
Non-cued
(stationary
ball
condition)
“Reach as fast as possible to
grasp a ball placed in the
contact zone”
(self-related instruction)
None Physical vs. virtual
target
Success rate
movement time
peak velocity
percentage of movement
time for acceleration
phase
PD slower than controls in movement time and peak
velocity
Cued
(moving
ball
condition)
not specified Visual
stimulus of
moving
target
PD similar to controls
42
3
3
Bie ńkie
wicz et
al., 2013
Forearm
flexion
and
extension
Non-cued
(self-paced
condition)
“participants were asked to
move as fast as they
comfortably could”
(self-related instruction)
None None Movement time
peak velocity
initiation time
ratio of time spent in the
acceleration phase
compared to deceleration
phase
PD slower than controls
Cue-guided
condition
1. Fast
2. Normal
3. Slow
(25% faster
and slower
based on a
movement
profile of a
non-
disabled
young
adults)
“participants were asked to
follow the light display with
their right index finger as
accurately as they could”
(self-related instruction)
Visual
stimulus of
moving
target
PD similar to controls
(no healthy older controls in the study, compare the
value with young adults)
3
4
Schlenst
edt et al.,
2017
Gait
initiation
Non-cued
(self-
initiated
gait)
“subjects were instructed to
initiate gait whenever they
were ready and with a
comfortable speed”
(self-related instruction)
None On and off
medication
COP excursion during
step initiation (max
medial-lateral distance),
step length
step duration
step velocity
PD did not perform worse during self-initiated
condition
Cued
(perceptual
cued gait)
“participants were instructed
to initiate gait as soon as they
felt the cue”
(non self-related instruction)
perceptual
cue of
movement
initiation
HC exhibited a more pronounced decrease in ML APA
size from the cued to the compensatory stepping
condition compared to PD (Med OFF).
compensato
ry forward
stepping
after
platform
perturbatio
n
“ participants were instructed
that the ground would move
under their feet and were
asked to do their best to
maintain balance without
anticipating the direction of
movement”
(self-related instruction)
external
perturbatio
n of the
platform
HC also exhibited a more pronounced increase in step
velocity than people with PD from cued to
compensatory stepping condition (No medication
effect on ML size of APA, step length, step velocity
and step duration)
43
CHAPTER 3
Experiment 1: The impact of enhanced efficacy expectation on motor performance and learning in
PD
Introduction
Parkinson’s disease (PD) is a neurodegenerative disease due to progressive dopamine depletion in
the substantia nigra. In addition to cardinal motor symptoms, individuals with PD also present with non-
motor features, including cognitive dysfunction and psychological disturbances. Recent studies have
started to focus on the impact of non-motor features on motor performance in PD. The contribution of
psychological factors to motor performance in PD has also begun to be revealed.
Among psychological factors, efficacy expectation, defined as a belief or expectation in one's
ability to produce actions thought important to succeed in specific situations, has been demonstrated to
have a profound influence on motor behavior (Bandura, 1977). Given that postural instability and gait
deficits are hallmark symptoms of PD, a number of studies have examined the relationship of balance and
gait performance with balance efficacy expectation using a cross-sectional study design. Balance efficacy
expectation (i.e. belief in one’s ability to maintain balance) has been shown to independently correlate
with postural stability (Adkin et al., 2003; Johnson et al., 2013; Lee et al., 2016), gait kinematics (Bryant
et al., 2014; Curtze et al., 2016; Mak & Pang, 2008; Nemanich et al., 2013; O’Connell & Guidon, 2016;
Rochester et al., 2008), turning velocity (Curtze et al., 2016), and fall history (Mak & Pang, 2009a), even
when disease severity and muscle strength are accounted for. Furthermore, perspective cohort studies
demonstrate that balance efficacy expectation can predict future fall and recurrent fall incidence in the
forthcoming six-month or one-year period (Almeida et al., 2016; Lindholm et al., 2015; Mak & Pang,
2009b). Although worsening motor impairments were not ruled out as a possible cause of falls, the
finding suggests that reduced balance efficacy expectation may interfere with balance and gait
performance, and lead to higher fall incidence in PD.
44
The traditional view with regards to how reduced efficacy expectation in non-disabled older
adults and individuals with PD may negatively impact balance and gait is through inducing activity
avoidance, de-conditioning and further motor function decline (Hadjistavropoulos, Delbaere, & Fitzgerald,
2011). However, empirical evidence in non-disabled adults suggests that efficacy expectation itself can
directly influence motor performance. For example, decreasing balance efficacy expectation by elevating
the platform height of a standing surface modified anticipatory postural control in non-disabled adults
(Adkin, Frank, Carpenter, & Peysar, 2002; Davis, Campbell, Adkin, & Carpenter, 2009). Clearly, for
individuals with PD, efficacy expectation decreases as motor capability declines. However, it is also
possible that a vicious cycle is created whereby declining motor capability reduces efficacy expectation,
which itself leads to a further decrement in performance. One way to break the cycle in order to optimize
motor performance and learning is to enhance efficacy expectation. Boosting efficacy expectation has
been shown to increase movement accuracy and efficiency in both young and older non-disabled adults
(McKay, Lewthwaite, & Wulf, 2012; Stoate, Wulf, & Lewthwaite, 2012; Wulf & Lewthwaite, 2016), but
this positive effect remains to be determined in individuals with PD. Given that enhanced efficacy
expectation is proposed to influence motor performance and learning via activating the dopaminergic
pathway (Wulf & Lewthwaite, 2016), it is likely that enhancing efficacy expectation could impact
performance in individuals with dopamine depletion, such as people with PD.
It is important to investigate whether enhanced efficacy expectation will facilitate motor
performance in PD, as well as motor learning. Therefore, a motor learning paradigm was used to
determine the short-term (acquisition) and long-term (retention) effect of enhanced efficacy expectation.
By addressing the influence of efficacy expectation, a potent and effective rehabilitation strategy may be
developed. Thus, the purpose of this study is to investigate the impact of enhanced efficacy expectation
on motor performance and learning in PD. We predicted that motor performance and learning in
individuals with PD would be enhanced with increased efficacy expectation.
45
Methods
Participants
Twenty five individuals diagnosed with Parkinson’s disease (13M/12F, mean age: 62.92 ± 9.16)
were recruited. Characteristics of the participants were mean Hoehn and Yahr stage 1.88 ± 0.44 during on
medication state with mean disease duration of 5.07 ± 3.97 years. Participants were excluded if they had
dementia as determined by the Montreal Cognitive Assessment (MoCA) score below 21(Hoops et al.,
2009), other neurological disorders, a need to use an assistive device during locomotion, or orthopedic
problems that prohibited them from performing the task. Before participation in the study, the subjects
signed an informed consent approved by the Institutional Review Board of University of Southern
California. All participants were blinded to the purpose of the study when initially enrolled. Participants
were then assigned into a Control group (n=12) and an Enhanced Expectancy group (n=13).
Stabilometer task
The participants practiced a novel dynamic balance task by balancing on a stability platform, also
known as stabilometer (Model 16030, Lafayette Instrument Company, Lafayette, IN). The task goal was
to keep the platform in a horizontal position for as long as possible during each 30-second trial. The
stabilometer is a 65 x 105 cm wooden platform on a hinge that can maximally deviate 15 degree to the
right and left sides. The range in which the platform moved left or right was recorded at a 25-Hz sampling
rate with the horizontal position set as 0 degree. While balancing on the platform, participants wore a
harness attached to an overhead suspension system (LiteGait model LG36E32, Mobility Research, Tempe,
AZ). The harness served as a safety precaution without providing body weight support. Each trial started
with an off-balance position in which the left side of the platform was maximally positioned to 15 degrees.
An auditory sound signaled the beginning of each trial. Participants were instructed to move the platform
to the horizontal position after hearing the starting signal. After each trial, performance feedback
quantified as time in balance was visually displayed on a computer monitor, as well as verbally informed
by the experimenter. Time in balance was calculated as the total amount of time when the platform was
46
within 5 degrees from the horizontal position. Participants were required to look straightforward to a
white wall in front of them. None of the participants had prior experience with the stabilometer task.
Prior to the acquisition phase, participants performed three familiarization pre-trials. During the
pre-trials, the task difficulty was titrated by placing bungee cords from the midline of the undercarriage of
the stability platform to each side of platform, as well as adjusting the tension of the cords. The task
difficulty was modified in this way for each individual to ensure that initial time in balance exceeded 5
seconds and that initial error, measured as the difference between the platform deviation and the
horizontal position, was below 10 degree. Feedback was not provided during the pre-trials.
Study procedure (Figure 3.1)
Before practicing the stabilometer task, baseline clinical assessments were performed, including
the Montreal Cognitive Assessment (MoCA), the Geriatric Depression Scale (GDS), the Activity-specific
Balance Confidence Scale (ABC) and MDS-Unified Parkinson’s disease Rating Scale (MDS-UPDRS).
The baseline clinical assessments were followed by the three pre-trials on the stabilometer, in which the
task difficulty was titrated. Once task difficulty was determined, the acquisition phase of the study began,
consisting of two baseline trials and fourteen practice trials. A 90-s seated rest was inserted between trials
to minimize fatigue. Feedback was given after each baseline and practice trial in the form of time in
balance. No additional feedback other than time in balance was given. Twenty-four hours after the
acquisition phase, participants returned to the laboratory for a retention test, which consists of seven trials
without feedback.
Self-efficacy manipulation
After the two baseline trials, the Enhanced Expectancy (EE) group received a verbal Enhanced
Expectancy statement from the experimenter, “At the beginning, it is common to have relatively big
movements of the platform. But, this is the type of task that you get better at with practice. Your
improvement across trials will reflect your learning and your getting the hang of it. This is a task that
people of all ages have learned to do well.” In addition to the Enhanced Expectancy statement, the EE
group was given time-in-balance criteria for successful performance, prior to the first practice trial and the
47
eighth practice trial. Specifically, before the first practice trial, the EE participants were informed that
balancing for 2 seconds longer than their performance at the 2
nd
baseline trial was considered good
performance for the first half of practice. The second time-in-balance criterion was given before the
eighth practice trial. The EE participants were informed that balancing for 3 seconds longer than their
performance at the 2
nd
baseline trial is considered good performance for the second half of practice. The
time-in-balance criteria were designed to boost the effect of the Enhanced Efficacy statement on self-
efficacy.
To determine whether the Enhanced Expectancy statement was effective in improving confidence,
self-efficacy was assessed before and after the Enhanced Expectancy statement. Three separate 10-cm
visual analog scales were used to assess the participants’ confidence to balance for 10, 15 and 20 seconds
in the very next trial (i.e. the first practice trial). The scale involved participants placing a vertical mark on
the scale. The 10-cm scale ranged from not confident at all on the left end to extremely confident on the
right end. Self-efficacy was evaluated again at the end of the acquisition phase and for a final time, before
the retention test. Additionally, participants were given a customized questionnaire (Chiviacowsky, Wulf,
Lewthwaite, & Campos, 2012; Lewthwaite & Wulf, 2010; Wulf et al., 2012, 2013) after the acquisition
phase and the retention test, to assess their motivation, enjoyment and affective experience on a 10-point
Likert scale with 1 indicating “not at all”, and 10 indicating “very”. The questionnaires provided on both
days were the same, with the exception of a single question. The questionnaire given after the retention
test did not include “How nervous were you while waiting for the feedback?” After completion of the
study, participants were given an exit interview to determine their perceived success for the stabilometer
task. At the end, participants were debriefed about the purpose of the study. The Control group went
through the same study procedure without receiving the Enhanced Expectancy statement, or time-in-
balance criteria.
48
Figure 3.1 Study procedure. All participants practiced a complex balance task on the first day. On the
second day, the participants returned for a retention test (see Methods section for detailed description).
The participants in the Enhanced Expectancy (EE) group received a verbal Enhanced Expectancy
statement and time-in-balance criteria (i.e. definitions of a “good” trial), while the participants in the
Control group received no statement nor performance criteria.
Outcome measures
The main outcome measure was time in balance, calculated as the total time when the platform
degree was within ± 5° from the horizontal level, to indicate overall balance performance. Additionally,
the mean power frequency (MPF) of the platform movement was calculated using a power spectrum
analysis to investigate the impact of enhanced self-efficacy on the control of the platform. Numerical
responses from both the self-efficacy scales and questionnaires were extracted.
Statistical analysis
The Shapiro-Wilk test was used to assess the normality of data. Based on the result of the
normality test, an independent t test was used to compare group differences in age, MoCA, ABC, GDS
and MDS-UPDRS. Chi-square analysis was conducted to assure no group differences in fall history and
sex.
To assess the impact of the Enhanced Expectancy statement on self-efficacy, the pre-post
statement change in self-efficacy rating was analyzed using a 2 group (Control vs. EE) x 3 performance
49
level (10, 15, and 20s) repeated measures ANOVA. Self-efficacy ratings for each time point (pre-
statement, post-statement, post-acquisition and pre-retention) were analyzed with a 2 group (Control vs.
EE) x 3 performance-level (10, 15, and 20s) repeated measures ANOVA.
Time in balance and mean power frequency in the two baseline trials were averaged and
compared with an independent t test to assure no group difference in baseline performance. To assess the
impact of the Enhanced Expectancy statement on motor performance, time in balance and mean power
frequency during the acquisition phase was compared with a 2 group (Control vs. EE) x 14 trials repeated
measures ANOVA. To test the impact on motor learning, a 2 group (Control vs. EE) x 7 trials repeated
measures ANOVA was used to compare time in balance and mean power frequency at retention between
groups.
Responses on the questionnaire were analyzed using a 2 group (Control vs. EE) x 2 time
(acquisition, retention) repeated measures ANOVA, with the exception of: “How nervous were you while
waiting for the feedback?” since this question was only relevant during acquisition. As such, an
independent t test was conducted to examine group difference. Pearson or Spearman’s correlation was
used to evaluate the relationship between time in balance and responses on the questionnaire based on the
normality of responses on the questionnaire. All statistical analyses were performed using PASW
Statistics 18.0 software (SPSS Inc., Chicago, IL), and alpha level was set at 0.05.
Results
The Control and EE groups did not differ in age, fall history, sex, MoCA, ABC, GDS and MDS-
UPDRS scores (Table 3.1).
Table 3.1. Demographics information in the Control and EE groups
Group Control EE p
N 12 13 --
Age 61.42 ± 9.57 64.31 ± 8.93 0.44
Sex (M/F) 7/5 6/7 0.41
Fall history (n) 3 1 0.27
50
Years since diagnosis (yrs) 5.73 ± 5.18 4.42 ± 2.16 0.41
MDS-UPDRS total 45.08 ± 24.02 36.73 ± 15.41 0.31
MDS-UPDRS motor 17.42 ± 12.09 16.00 ± 8.86 0.74
ABC 88.34 ± 9.05 89.03 ± 8.91 0.85
MoCA 26.75 ± 3.17 27.54 ± 1.71 0.46
GDS 5.75 ± 6.11 4.77 ± 5.13 0.67
Value is reported as mean ± standard deviation. MDS-UPDRS, MDS Unified Parkinson's Disease Rating
Scale (max total score= 260, max motor score= 132); ABC, Activity-specific Balance Confidence scale
(max score=100); MoCA, Montreal Cognitive Assessment (max score=30; normal ≥ 26); GDS, Geriatric
Depression Scale (max score=30; normal ≤ 9).
Self-efficacy change in response to the Enhanced Expectancy statement
No significant performance-level (F
1.322, 30.404
= 1.14, p = 0.31) or group effects (F
1, 23
= 0.73, p =
0.40) were observed on self-efficacy change after the statement. There was a borderline significant group
by performance-level interaction (F
1.322, 30.404
= 3.02, p = 0.08). The group difference on self-efficacy
change was the largest for the most difficult performance level (i.e. balancing for 20 s). The EE group had
an increase in self-efficacy while the Control group showed no change (Figure 3.2).
Figure 3.2 Changes in self-efficacy rating after the Enhanced Expectancy statement between the Control and EE groups.
Error bar indicates standard error.
51
Self-efficacy ratings at acquisition and retention
A significant performance-level effect was observed on self-efficacy rating (p<0.01) at all time
points (pre-statement, post-statement, post-acquisition and pre-retention). Participants rated lower self-
efficacy with more difficult performance levels. There was no group effect (p>0.05) or group by
performance-level interaction (p>0.05) on self-efficacy ratings at any time point (Figure 3.3).
Figure 3.3 Self-efficacy ratings at acquisition and retention in the Control and EE groups
Time in balance
The baseline time in balance was not different between the EE and Control groups (t
23
= 0.34, p =
0.74). Throughout acquisition, both groups improved significantly in time in balance (F
7.506, 165.127
= 3.13,
p < 0.01). No significant group main effect (F
1, 23
= 0.34, p =0.57) or group by trial interaction (F
7.506,
165.127
= 0.71, p =0.67) was observed.
52
A similar result was found on time in balance at retention. There was a significant trial effect
(F
3.878, 89.203
= 4.87, p < 0.01) without a significant group effect (F
1, 23
= 0.18, p = 0.67) or group by trial
interaction (F
3.878, 89.203
= 1.13, p = 0.35) (Figure 3.4).
Figure 3.4 Time in balance at acqusition and retention in the Control and EE groups
Mean power frequency (MPF)
The EE and Control groups did not differ in baseline MPF (t
23
= 1.23, p = 0.22). There was no
significant trial effect (F
13, 299
= 1.20, p = 0.28), group effect (F
1, 23
= 1.12, p = 0.30) or group by trial
interaction (F
13, 299
= 1.66, p =0.07) on MPF during acquisition. A similar finding was observed on MPF
at retention. No trial effect (F
6, 138
= 0.50, p = 0.81), group effect (F
1, 23
= 2.97, p = 0.10) or group by trial
interaction (F
6, 138
= 1.26, p = 0.28) was observed (Figure 3.5).
Baseline Acquisition Retention
53
Figure 3.5 Mean power frequency at acqusition and retention in the Control and EE groups
Responses to the questionnaire
Questionnaire results are summarized in Table 3.2. No significant time effect (acquisition vs.
retention) was observed on response for most of the questions. For the question “how concerned were you
about your performance”, both the EE and Control groups reported that they were less concerned at
retention than at acquisition (F
1, 22
= 6.08, p = 0.02).
There was no significant group difference in the questionnaire results, with the exception of those
questions related to nervousness. The EE group reported a higher nervousness at the beginning of each
trial than the Control group, at both acquisition and retention (F
1, 22
= 7.17, p = 0.01). A higher
nervousness while waiting for feedback at acquisition was also reported by the EE group compared with
the Control group with a borderline significance (t
22
= 1.28, p = 0.07).
Table 3.2 Responses in the questionnaire completed at the end of acquisition and retention in the
Control and EE groups.
Questions
EE Control
Acquisition Retention Acquisition Retention
How motivated were you to practice this task? 8.15 (1.63) 8.62 (1.19) 8.58 (1.51) 8.83 (1.53)
How much did you enjoy practicing this task? 7.69 (1.89) 8.15 (1.57) 7.58 (2.78) 7.83 (2.86)
How satisfied were you with your performance? 4.69 (1.80) 5.46 (2.22) 5.33 (2.10) 5.92 (2.43)
How concerned were you about your performance?* 6.46 (1.85) 5.92 (2.14) 6.50 (3.15) 4.33 (3.03)
How much did you think about your ability on this task
while balancing today?
7.08 (2.22) 7.23 (1.59) 6.58 (2.54) 6.00 (2.00)
Baseline
Acquisition Retention
54
How nervous were you before the start of each trial?
#
4.00 (2.00) 4.31 (2.46) 2.17 (1.03) 2.08 (1.83)
How nervous were you while waiting for the feedback? 5.08 (2.87) ─ 3.00 (2.17) ─
How confident were you about your ability on this task? 5.46 (1.51) 5.62 (1.71) 5.58 (2.35) 6.08 (2.54)
Significant differences between accusation and retention are indicated by *. Significant differences between groups are indicated by
#
.
Relationship between questionnaire responses and retention performance
The higher nervousness reported by the EE group in comparison with that of the Control group
was an interesting and unexpected finding. We would like to explore a possibility that increased
nervousness in the EE group may have counteracted the impact of enhanced self-efficacy, resulting in no
superior performance compared to the Control group. Recall that the EE group showed a borderline
significant increase in self-efficacy rating particularly for the 20-s time period. To test our speculation,
Spearman’s correlations were used to investigate the relationships between retention time in balance, and
responses on the nervousness questions (i.e. “nervousness while waiting for feedback” at acquisition;
“nervousness before the start of each trial” at acquisition and retention). A negative correlation with
borderline significance was found between retention time in balance and nervousness before the start of
each trial at acquisition, only in the EE group (Control, r
s
= 0.11, p= 0.97; EE, r
s
= -0.51, p= 0.08; Figure
3.6). Participants who reported higher nervousness before the start of each trial at acquisition had worse
retention performance, which supported our suspicion. No significant correlations were observed between
retention time in balance with the other two nervousness questions (“nervousness before the start of each
trial” at retention, Control: r
s
= 0.02, p = 0.96, EE: r
s
= -0.39, p = 0.19; “nervousness while waiting for
feedback” at acquisition, Control: r
s
= 0.37, p = 0.24, EE: r
s
= -0.42, p = 0.16).
55
Figure 3.6 Correlation between reported nervousness before the start of each trial at acquisition and retention time in
balance between the Control and EE groups
Interestingly, when asked: “how confident were you about your ability on this task”, the EE
group did not report a higher confidence compared to the Control group, even though their self-efficacy
was enhanced at early acquisition, as evidenced by the result of the changes in self-efficacy rating. In
addition, to further investigate whether response to the confidence question may have a positive effect on
retention performance, we used correlation to assess whether reported confidence at the end of acquisition
was associated with retention performance. There was a positive correlation across both groups between
confidence at the end of acquisition and retention time in balance (r = 0.42, p = 0.04). The result
suggested that participants who reported higher confidence at the end of acquisition had better retention
performance. No significant correlation was found between nervousness and confidence reported in the
post-acquisition questionnaire (r
s
= -0.19, p = 0.36), suggesting that confidence and nervousness measure
distinct constructs and may impact performance independently.
Potential sources of increased nervousness
56
To distinguish whether the higher nervousness in the EE group indicated an increase in state
anxiety (defined as a temporary disturbing emotional arousal as a result of recognition of a disturbing
stimulus) or trait anxiety (referring to a stable tendency to attend to, experience, and report negative
emotions such as fears, worries, and anxiety across many situations ), we examined whether the EE and
Control group differed in the UPDRS anxiety score, which may reflect trait anxiety. We found no
difference in the UPDRS anxiety score between groups (Control vs. EE: 0.75 ± 0.87 vs. 0.77 ± 0.83, p =
0.96), suggesting that the higher nervousness in the EE group may be related to an increase in state
anxiety.
Next we tested whether the high nervousness in the EE group may be a result of actual
performance success. Success rate during acquisition was calculated in both the Control and EE groups by
applying time-in-balance criteria to time in balance at acquisition. The mean success rate was below 50%
in both groups, but there was no group difference (EE vs. Control: 38% vs. 35%; t
22
= 0.26, p = 0.78).
Interestingly, the EE group reported a lower perceived success rate than the Control group in the exit
interview (EE vs. Control: 25% vs. 41%; t
21
= 1.77, p = 0.09; n = 23, 1 missing data due to no perceived
success rate reported in one EE participant). Overall the EE group perceived that they had performed well
in fewer trials during acquisition, compared to the Control group.
More importantly, the perceived success rate was negatively correlated with nervousness reported
at the end of acquisition (r
s
= -0.54, p < 0.01). However, no correlation was observed between the actual
success rate determined by time-in-balance criteria, and nervousness (r
s
= -0.12, p = 0.59). The result
suggested that the high nervousness level in the EE participants was primarily driven by low perceived
success, rather than their actual success across acquisition. Given that time-in-balance criteria served as
the standard for the EE participants to judge whether their performance was considered “good” (i.e.
successful) or not, performance criteria may be the potential source of nervousness. EE participants may
get nervous at acquisition when their time in balance did not reach the so-called “good” performance.
57
Discussion
The goal of this study was to determine whether enhanced efficacy expectation would improve
motor performance and learning in individuals with PD when they practiced a novel dynamic balance task.
Our finding showed that the Enhanced Expectancy statement appeared to be effective in boosting self-
efficacy in individuals with PD, especially their confidence in achieving high performance level (i.e.
balancing for 20 s). Contrary to our prediction, this enhanced self-efficacy did not appear to impact motor
performance and learning. The EE group did not show longer time in balance compared to the Control
group, both at acquisition and retention. Similarly, there was no group difference on the mean power
frequency of the platform movements, suggesting that the EE group did not adjust the platform more
automatically than their counterpart. Surprisingly, the EE group reported a higher nervousness level than
the Control group at acquisition and retention. It was speculated that the increased nervousness may
interfere with task performance and counteract the positive impact of enhanced self-efficacy. Based on
the questionnaire, a negative relationship between nervousness and retention performance, in combination
with a positive correlation between confidence and retention performance, supported the above
assumption.
Increase in self-efficacy as a result of the EE statement was the most substantial for the highest
performance level (i.e. balancing for 20-s), supporting the notion that the EE statement enhanced
participants’ expectations for future success. When comparing the change in self-efficacy rating before
and after the statement, EE participants reported more confidence that they could balance for 20 seconds
on the next trial, even though they had not reached the 20-s level at baseline. We also observed that after
the statement, the EE group reported a numerically higher self-efficacy rating than the Control group for
the 15-s and 20-s levels. Despite the fact that the self-efficacy rating for the EE group was not
significantly higher compared to the Control group, 7/13 (54%) participants were above the median self-
efficacy rating across all participants. The between-group difference in self-efficacy rating in this study
was not as large as what has been reported in non-disabled older adults using a similar verbal enhanced
expectancy statement and a stabilometer task (Wulf et al., 2012). In Wulf et al. (2012), non-disabled older
58
women showed a significantly higher self-efficacy rating for the 20-s level than their counterpart after
receiving the enhanced expectancy statement and performing one trial of the stabilometer task. The
smaller group difference in self-efficacy rating observed in this study compared to that reported by Wulf
(2012) may be due to different study protocols, in particular the size of the stabilometer platform, which
was much larger than the platform used in this study, as well as the time at which self-efficacy was
assessed in relation to when the statement was given.
An initial interpretation of our data may be inferred based on the Social Cognitive theory by
Bandura (Bandura, 1977). Mastery experience, vicarious experience of observing the attainments of
others ("If they can do it, I can do it as well"), social persuasion (“you can do this”), as well as
physiological and emotional states are the four sources of self-efficacy (Bandura, 1977). Among those
factors, mastery experience has the strongest influence on self-efficacy, while the impact of social
persuasion is relatively weaker. It is possible that the impact of social persuasion delivered by the EE
statement on self-efficacy may require some time for the information to be internalized, or that the
statement needs to be confirmed with actual performance achievement. In this study, self-efficacy was
measured right after the statement, while in Wulf et al. (2012), the participants rated self-efficacy after
receiving the statement but having performed one trial of the task. The experience of performing the task
may provide a validation for the enhanced expectancy statement, and thereby induce a larger increase in
self-efficacy in the enhanced expectancy group, which is sufficient for their self-efficacy rating to be
distinct from the control group. Hence, although in this study we observed an immediate boost in self-
efficacy after the statement, the amount of increase may not have been enough to create a distinct
difference in self-efficacy ratings between groups, due to the timing of self-efficacy assessment (i.e. after
the statement, but before participants actually performed to validate the EE statement they received). This
result is also consistent with one study showing that receiving positive social-comparative feedback a
single time did not significantly increase self-efficacy immediately afterwards in non-disabled young
adults (Lamarche, Gammage, & Adkin, 2011).
59
Alternatively, it is possible that a stronger self-efficacy boost than that afforded by social
persuasion is required in order to increase self-efficacy in individuals with diagnosis of progressive
disease, such as PD. One study conducted by Pasman et al. attempted to enhance balance self-efficacy in
individuals with PD by providing additional safety features during quiet standing on ground level
(Pasman et al., 2011). The act of providing additional physical safety features failed to increase
participants’ self-efficacy. Given that PD is a neurodegenerative disease, individuals may expect
performance deterioration, or they may attribute improved performance to medication, rather than
believing in their own capability. Hence, it is likely that the immediate response to the EE statement may
be smaller in individuals with PD because they may not expect they are capable of performing better,
especially for a challenging balance task. In addition, individuals with PD may have prior experience with
regards to performance deterioration, postural instability and falls. It may take more time for the
statement to be internalized in order to improve self-efficacy, or a stronger manipulation may be required,
compared to non-disabled older adults. Further, it is not clear whether common non-motor symptoms in
PD, such as apathy, depression and anxiety, may influence their response to the enhanced expectancy
statement. Another possibility may be the issue of credibility, namely how believable the statement was to
the participants. In the exit interview, the EE participants were also asked what they thought about the EE
statement. Among the 13 EE participants, 10 participants (77%) reported that the statement was true and
they agreed with it. Thus, the exit interview confirmed that the statement was convincing to most of the
EE participants.
Both the EE and Control groups improved on time in balance at acquisition and retained their
performance one day after. However, the EE group did not outperform the Control group at acquisition or
retention. The finding did not support our original hypothesis that enhanced self-efficacy would improve
motor performance and learning in individuals with PD. Before coming to a conclusion, we would like to
explore whether there is an alternative explanation for the negative finding. There was an unexpected
finding in the questionnaire results. Compared to the Control group, the EE group reported higher state
anxiety (i.e. nervousness) at both acquisition and retention. We speculated that increased anxiety in the
60
EE group may negatively influence task performance and mask the positive effect of enhanced self-
efficacy. The above suspicion was supported by a negative association between anxiety and retention
performance.
Inducing anxiety related to fear of falling has been shown to degrade motor performance in PD,
manifested by a slowed and variable gait, and increased freezing episodes (Caetano, Gobbi, Sánchez-
Arias, Stella, & Gobbi, 2009; Doan et al., 2010, 2013, Ehgoetz Martens, Ellard, & Almeida, 2014, 2015).
Similarly, increasing social anxiety while being assessed by an evaluator can lead to decrement in balance
performance in non-disabled older adults (Geh, Beauchamp, Crocker, & Carpenter, 2011). We reasoned
that one potential source of anxiety may be the time-in-balance criteria. The low perceived success rate
reported by the EE group, as well as its association with nervousness, suggested that the performance
criteria in this study may increase state anxiety, and thus disrupt motor performance and learning. We
further tested the above hypothesis in Experiment 2.
Experiment 2: The impact of state anxiety resulting from performance criteria on motor
performance and learning in PD
Introduction
In addition to the enhanced expectancy statement (McKay et al., 2012; Wulf et al., 2012, 2013),
three common ways used to enhance self-efficacy in the experimental settings are: (1) positive social-
comparative feedback (Lamarche et al., 2011; Pascua et al., 2015; Wulf et al., 2014, 2012; Wulf,
Lewthwaite, Cardozo, & Chiviacowsky, 2017; Wulf et al., 2013), (2) feedback after relatively good
performance (Saemi et al., 2012), and (3) relatively easy performance criteria (Chiviacowsky & Harter,
2015; Chiviacowsky, Wulf, & Lewthwaite, 2012; Ong, Lohse, & Hodges, 2015; Palmer, Chiviacowsky,
& Wulf, 2016; Trempe, Sabourin, & Proteau, 2012). It has been demonstrated that non-disabled young
adults who were given relatively easy criteria to reach what was considered successful performance at
acquisition reported higher self-efficacy, and better motor performance, especially at retention, than
participants who received relatively difficult criteria (Chiviacowsky & Harter, 2015; Chiviacowsky, Wulf,
& Lewthwaite, 2012; Palmer et al., 2016). Across studies, the success rate of the participants who were
61
informed of relatively easy criteria ranged between 22-58%. While the success rate of the participants
who received relatively difficult criteria ranged between 2-8%.
Interestingly, the finding described above from the Chiviacowsky et al. (2015) study, indicates
that the very presence of performance criteria regardless of how easy it is may be has an impact on motor
learning. In the study (Chiviacowsky & Harter, 2015), the high success group who received relatively
easy performance criteria did not show better motor performance and learning, compared to the control
group who received no performance criteria, even though the high success group reported a higher self-
efficacy. The result suggests that the existence of performance standards may attenuate the beneficial
impact of enhanced self-efficacy. Consistent with the above finding, it has been shown that the feeling of
being assessed by an evaluator disrupted balance performance in non-disabled older adults via increasing
social anxiety (Geh et al., 2011).
Thus, to test whether increased state anxiety as a result of performance criteria attenuated the
impact of enhanced self-efficacy, we took an approach to provide individuals with PD the enhanced
expectancy statement without the performance criteria. We hypothesized that (1) state anxiety would be
reduced by removing performance criteria and (2) enhanced self-efficacy without simultaneously
inducing state anxiety would improve motor performance and learning in PD.
Methods
Participants
Twenty four individuals diagnosed with Parkinson’s disease (14M/10F, mean age: 63.04 ± 9.99)
were included in the study. Characteristics of the 24 participants were mean Hoehn and Yahr stage 1.96 ±
0.36 during “on” medication state with mean disease duration of 4.42 ± 4.34 years. The participants in
Experiment 2 included the 12 control participants from Experiment 1 and 12 individuals with Parkinson’s
disease who were additionally recruited. The recruited 12 participants were assigned to an Enhanced
Expectancy without definition group (EE-noD). Participants were excluded if they had dementia as
determined by the Montreal Cognitive Assessment (MoCA) score below 21 (Hoops et al., 2009), other
62
neurological disorders, a need to use an assistive device during locomotion, or orthopedic problems that
prohibited them from performing the task. Before participation in the study, the subjects signed an
informed consent approved by the Institutional Review Board of University of Southern California.
Stabilometer task
As described in Experiment 1, the participants practiced the stabilometer task (Model 16030,
Lafayette Instrument Company, Lafayette, IN) with a task goal of keeping the platform in a horizontal
position for as long as possible during each 30-second trial. While balancing on the platform, participants
wore a harness attached to an overhead suspension system (model LG36E32, Mobility Research, Tempe,
AZ) as a safety precaution without providing body weight support. Each trial started from an off-balance
position in which the left side of the platform rested against the frame, which was maximally positioned
to15 degrees. After each trial, performance feedback quantified as time in balance was visually displayed
on a computer monitor, as well as orally informed by the experimenter. Participants were required to look
straightforward to a white wall in front of them. Participants did not have prior experience with the
stabilometer task. As in Experiment 1 (see page 48), participants performed three familiarization pre-trials
and stabilometer difficulty was titrated for each individual.
Study protocol (Figure 3.1)
As in Experiment 1, the participants received baseline clinical assessments, including the
Montreal Cognitive Assessment (MoCA), the Geriatric Depression Scale (GDS), the Activity-specific
Balance Confidence Scale (ABC) and MDS-Unified Parkinson’s disease Rating Scale (MDS-UPDRS).
The baseline clinical assessments were followed by the three pre-trials on the stabilometer, in which the
task difficulty was titrated. Once task difficulty was determined, the acquisition phase of the study began,
consisting of two baseline trials and fourteen practice trials. A 90-s seated rest was inserted between trials
to minimize fatigue. Feedback was provided after each baseline and practice trial in the form of time in
balance. No additional feedback other than time in balance was given. Twenty-four hours after the
acquisition phase, participants returned for a retention test, which consists of seven trials without
feedback.
63
Self-efficacy manipulation
After the two baseline trials, the Enhanced Expectancy without definition group (EE-noD) only
received the verbal Enhanced Expectancy statement as in Experiment 1 from the experimenter, “At the
beginning, it is common to have relatively big movements of the platform. But, this is the type of task that
you get better at with practice. Your improvement across trials will reflect your learning and your getting
the hang of it. This is a task that people of all ages have learned to do well.”
Outcome measures
The outcome measures were (1) time in balance; (2) mean power frequency (MPF) of the
platform movement; (3) self-efficacy ratings and (4) responses to the questionnaire and exit interview.
Statistical analysis
The Shapiro-Wilk test was used to assess the normality of data. Based on the result of the
normality test, an independent t test was used to compare group differences in age, MoCA, ABC, GDS
and MDS-UPDRS. Chi-square analysis was conducted to assure no group differences in fall history and
sex.
To assess the impact of the Enhanced Expectancy statement on immediate changes in self-
efficacy, the pre-post statement change in self-efficacy rating was analyzed using a 2 group (Control vs.
EE-noD) x 3 performance level (10, 15, and 20s) repeated measures ANOVA. Self-efficacy ratings for
each time point (pre-statement, post-statement, post-acquisition and pre-retention) were analyzed with a 2
group (Control vs. EE-noD) x 3 performance-level (10, 15, and 20s) repeated measures ANOVA.
Time in balance and mean power frequency in the two baseline trials were averaged and
compared with an independent t test to assure no group difference in baseline performance. To assess the
impact of the Enhanced Expectancy statement on motor performance, time in balance and mean power
frequency during the acquisition phase was compared with a 2 group (Control vs. EE-noD) x 14 trials
repeated measures ANOVA. To test the impact on motor learning, a 2 group (Control vs. EE-noD) x 7
trials repeated measures ANOVA was used to compare time in balance and mean power frequency at
retention between groups.
64
Responses on the questionnaire were analyzed using a 2 group (Control vs. EE-noD) x 2 time
(acquisition, retention) repeated measures ANOVA, with the exception of: “How nervous were you while
waiting for the feedback?” since this question was only relevant during acquisition. As such, an
independent t test was conducted to examine group difference. All statistical analyses were performed
using PASW Statistics 18.0 software (SPSS Inc., Chicago, IL), and alpha level was set at 0.05.
Results
The demographics of the EE-noD group were not significantly different from that of the Control
group (Table 3.3).
Table 3.3 Demographics information in the Control and EE-noD groups
Group Control EE-noD p
N 12 12 --
Age 61.42 ± 9.57 64.67 ± 10.55 0.43
Gender (M/F) 7/5 7/5 1.00
Fall history (n) 3 4 0.65
Years since diagnosis (yrs) 5.73 ± 5.18 3.12 ± 2.98 0.15
MDS-UPDRS total 45.08 ± 24.02 37.83 ± 17.45 0.41
MDS-UPDRS motor 17.42 ± 12.09 15.83 ± 7.35 0.70
ABC 88.34 ± 9.05 91.29 ± 9.40 0.44
MoCA 26.75 ± 3.17 27.55 ± 2.73 0.45
GDS 5.75 ± 6.11 3.67 ± 2.84 0.30
Value is reported as mean ± standard deviation. MDS-UPDRS, MDS Unified Parkinson's Disease Rating
Scale (max total score= 260, max motor score= 132); ABC, Activity-specific Balance Confidence scale
(max score=100); MoCA, Montreal Cognitive Assessment (max score=30; normal ≥ 26); GDS, Geriatric
Depression Scale (max score=30; normal ≤ 9).
Self-efficacy change in response to the Enhanced Expectancy statement
There was a significant performance-level effect (balancing for 10, 15 and 20 s; F
1.295, 28.492
= 4.22,
p = 0.04) on self-efficacy change after the statement. For both the Control and EE without definition
groups, self-efficacy for the 10-s performance level increased after the statement and reduced when the
performance level was higher. No group by performance level interaction (F
1.295, 28.492
= 1.43, p = 0.25) or
65
group effect (F
1, 22
= 0.17, p = 0.68) were observed (Figure 3.7). Even though there was no significant
group effect or interaction, the EE-noD group showed a positive change for the 20-s performance level,
whereas the Control group showed a negative change.
Figure 3.7 Changes in self-efficacy rating after the Enhanced Expectancy statement between the Control and EE-noD
groups. Error bar indicates standard error
Self-efficacy ratings at acquisition and retention
A significant performance-level effect was observed on self-efficacy rating (p<0.01) at all time
points (pre-statement, post-statement, post-acquisition and pre-retention; see Figure 3.1 for study
procedure). Participants’ self-efficacy decreased with more difficult performance levels. There was no
group effect (p>0.05) or group by performance-level interaction (p>0.05) on self-efficacy ratings at any
time point (Figure 3.8). Regardless, at the end of acquisition, the EE-noD group showed numerically
higher self-efficacy than the Control group, especially for the 10-s and 15-s performance levels.
66
Figure 3.8 Self-efficacy ratings at acquisition and retention in the Control and EE-noD groups
Time in balance
The baseline time in balance was not different between the EE-noD and Control groups (t
22
= -
1.13, p = 0.27). Throughout acquisition, both groups improved significantly in time in balance (F
13, 286
=
2.91, p < 0.01). There was no group main effect (F
1, 22
= 1.16, p =0.29) or group by trial interaction (F
13,
286
= 2.91, p = 0.91).
For time in balance at retention, there was a borderline significant trial effect (F
3.595, 79.083
= 2.48, p
= 0.06). No significant group by trial interaction was observed (F
3.595, 79.083
= 0.75, p = 0.55). There was a
borderline-significant group effect on time in balance at retention (F
1, 22
= 2.94, p = 0.10, η
p
² = 0.12)
(Figure 3.9).
67
Figure 3.9 Time in balance at acqusition and retention in the Control and EE-noD groups
Mean power frequency (MPF)
The EE-noD and Control groups did not differ in baseline MPF (t
22
= -1.25, p = 0.23). There was
no trial effect (F
7.032, 154.696
= 0.67, p = 0.70) or group effect (F
1, 22
= 2.18, p = 0.15) on MPF during
acquisition. However, there was a significant group by trial interaction (F
7.032, 154.696
= 2.25, p =0.03) on
MPF during acquisition.
For MPF at retention, a borderline significant trial effect was observed (F
6, 132
= 1.87, p = 0.09).
There was no significant group effect (F
1, 22
= 0.80, p = 0.38) or group by trial interaction (F
6, 132
= 1.68, p
= 0.13, Figure 3.10).
Figure 3.10 Mean power frequency at acqusition and retention in the Control and EE-noD groups
Baseline Acquisition Retention
Baseline Acquisition Retention
68
Response to the questionnaire and exit interview
Questionnaire results are summarized in Table 3.4. No significant group difference was observed
on any of the questions in the questionnaires. Importantly, the nervousness level reported by the EE-noD
group was not different than the Control group.
No significant time effect (at the end of acquisition and retention) was observed on responses to
most of the questions. For the question “how satisfied were you about your performance”, participants
reported that they were more satisfied at retention than at acquisition for both the EE-noD and Control
groups (F
1, 22
= 8.04, p = 0.01). For the question “how much do you think about your ability on this task
while balancing today”, participants reported that they thought less frequently about their ability at
retention than at acquisition for both groups (F
1, 22
= 8.04, p = 0.04). The perceived success rate reported
in the exit interview was not different between the Control and EE-noD groups (EE-noD vs. Control: 41%
vs. 41%; t
22
= -0.01, p = 0.99). The actual success rate was also not different between the two groups (EE-
noD vs. Control: 39% vs. 38%; t
22
= -1.37, p = 0.89).
Table 3.4 Responses in the questionnaire completed at the end of acquisition and retention in the
Control and EE-noD groups.
Questions
EE-noD Control
Acquisition Retention Acquisition Retention
How motivated were you to practice this task? 9.33 (0.89) 9.42 (0.79) 8.58 (1.51) 8.83 (1.53)
How much did you enjoy practicing this task? 9.08 (1.24) 9.17 (0.94) 7.58 (2.78) 7.83 (2.86)
How satisfied were you with your performance?* 6.08 (1.68) 7.33 (0.98) 5.33 (2.10) 5.92 (2.43)
How concerned were you about your performance? 5.50 (2.39) 5.50 (2.68) 6.50 (3.15) 4.33 (3.03)
How much did you think about your ability on this task
while balancing today?*
7.25 (2.45) 5.75 (2.77) 6.58 (2.54) 6.00 (2.00)
How nervous were you before the start of each trial?
1.92 (1.24) 2.58 (2.19) 2.17 (1.03) 2.08 (1.83)
How nervous were you while waiting for the feedback? 2.17 (1.64) ─ 3.00 (2.17) ─
How confident were you about your ability on this task? 6.42 (1.38) 7.08 (1.73) 5.58 (2.35) 6.08 (2.54)
Significant differences between acquisition and retention are indicated by *.
Control experiment in non-disabled older adults
In order to investigate whether increased nervousness in response to performance criteria is a
common reaction or specific to individuals with PD, we conducted a control experiment by applying the
same study design with non-disabled older adults. Twenty-seven non-disabled older adults (10M/17F,
69
mean age: 62.26 ± 7.22) were recruited and assigned into the Control (n=11), EE (n=8), and EE-noD (n=8)
groups. The non-disabled older participants went through the same study procedure as participants with
PD, except that the stabilometer task difficulty was not titrated. Given that during the three pre-trials, non-
disabled older adults exceeded the criteria we set for initial performance (i.e. time in balance longer than
5 seconds and averaged error below 10), no bungee cord was added.
The questionnaire results showed that the EE group reported a higher nervousness level before
the start of each trial at acquisition and retention, than the Control group (F
1, 17
= 5.87, p = 0.03) and EE-
noD group (F
1, 14
= 4.14, p = 0.06). There was no difference in the nervousness level between the Control
and EE-noD participants (F
1, 17
= 0.10, p = 0.75). The magnitude of the nervousness level reported by
non-disabled older participants did not differ from their PD counterparts (p> 0.05). The result indicated
that the increase of state anxiety in response to performance criteria was a general phenomenon and not
exclusive in individuals with PD.
Discussion
The main finding of Experiment 2 was that the EE-noD participants reported a comparable
nervousness level with the Control participants, supporting our assumption that performance criteria
induced state anxiety in individuals with and without PD. We observed a trend whereby the EE-noD
group had longer time in balance than the Control group at retention, suggesting that enhancing self-
efficacy without simultaneously inducing state anxiety appeared to improve motor learning in individuals
with PD.
The original purpose of adding performance criteria was to increase the effectiveness of the
Enhanced Expectancy statement on self-efficacy. The criteria of 2-s and 3-s longer than an individual’s
baseline performance were determined based on one study investigating motor learning in individuals
with PD using the stabilometer task with a larger platform (Chiviacowsky, Wulf, Lewthwaite, et al.,
2012). In the study, participants with PD improved time in balance by 4 seconds after 10 trials of practice.
The smaller stabilometer platform (65 x 105 cm) in our study compared to the one used in the previous
70
study (130 x 140 cm) may increase the task difficulty and limit the amount of improvement across trials,
even though the task difficulty was titrated before acquisition. The 2-s and 3-s criteria may be too
challenging and discouraging, as indicated by the fact that the actual success rates of three groups were all
below 50% (below chance). It is interesting to notice that in most of the studies utilizing performance
criteria as self-efficacy manipulation, the group that received relatively easy criteria succeeded in more
than half of the trials (53-58%). It is not clear whether the impact of performance criteria will act to
improve performance, as our original intention if the standards are set at a lower level to allow higher
success rates (above 50%).
Our finding indicated that there was a trend whereby enhanced self-efficacy appeared to facilitate
motor learning in individuals with PD. The EE-noD and Control groups showed similar time in balance at
acquisition, but the difference between the two groups became more prominent at retention. The result
corresponds to the previous findings in non-disabled adults that the positive impact of enhanced self-
efficacy was more obvious in motor learning than motor performance when individuals acquired a motor
skill (Chiviacowsky & Harter, 2015; Trempe et al., 2012; Wulf et al., 2012). The effect size in our study
quantified by partial Eta squared ( η
p
²) is 0.12, which is considered moderate (Cohen, 1988). The effect
size in this study is comparable, although slightly smaller, with the reported effect size in non-disabled
older adults (Wulf et al., 2012)( η
p
² = 0.18). Hence, we argue that the trend we observed represents a true
difference that was not detectable with the sample size used in this study. A post-hoc power analysis with
the current result was conducted and 31 participants per group would be required to reach a power of 0.80.
So while increasing self-efficacy without concomitantly inducing nervousness appeared to
enhance motor learning in people with PD, it is important to address the fact that we only observe a trend.
Our finding may be due to neuropathology associated with dopamine depletion, which is evident in
people with PD. Based on the placebo literature and considering that feeling capable is a rewarding
experience, we suspect that self-efficacy may modulate motor performance and learning via dopaminergic
pathways (Wulf & Lewthwaite, 2016), especially the reward circuit (Lidstone et al., 2010; Schmidt et al.,
2014). Even though the level of dopamine depletion is the most marked in the dorsolateral striatum,
71
leaving the ventromedial striatum relatively preserved (de la Fuente-Fernández, 2013; Kish et al., 1988),
the reward circuit may be affected even in early disease stage (Scatton, Rouquier, Javoy-Agid, & Agid,
1982; Schott et al., 2007). The majority of the participants (15 out of 24) in Experiment 2 were within 5
years of diagnosis (years since diagnosis ranging from 3 months to 20 years among all 24 participants).
The disrupted dopaminergic pathways in PD may influence two processes: (1) how reward-related
information is processed and (2) how reward-related information modifies motor areas to promote motor
performance and learning (Pessiglione et al., 2007). Findings from neuro-imaging studies in PD suggest
that the latter case may be more likely (Kapogiannis et al., 2011; Schonberg et al., 2010; Shiner, Seymour,
Wunderlich, et al., 2012). It has been shown that reward-induced changes in dorsolateral striatum as well
as in motor cortex excitability were present in non-disabled older adults, but absent in individuals with
PD, even when they were medicated with levodopa (Kapogiannis et al., 2011; Schonberg et al., 2010).
The findings from the placebo literature in PD are also consistent with the above speculation. Frisaldi et al.
(2014) demonstrated that placebo-induced motor improvement in individuals with PD was associated
with the amount of dopamine release in the dorsal striatum, while such correlation was not observed with
dopamine release in the ventral striatum (Frisaldi et al., 2014). Our finding of improvement in motor
learning with self-efficacy enhancement indicates that self-efficacy can be modulated to enhance motor
learning in individuals with PD, but the appropriate parameters for “dose” of self-efficacy enhancement
may need to be understood in order to promote motor learning with disrupted dopaminergic systems.
Several limitations in the study should be noted. First, no neutral statement was provided to the
Control group. We cannot rule out the possibility that superior performance in the EE-noD group may be
due to the very presence of the Enhanced Expectancy statement, independent of the content of the
statement. The EE-noD participants may have tried harder because they received more attention from the
experimenter. However, we argue that the above explanation may be less likely given that the EE group
in Experiment 1 also received the Enhanced Expectancy statement, but did not differ in their performance
compared to the Control group. Second, participants’ state anxiety was not assessed at the beginning of
acquisition. Even though the finding from the EE-noD group in Experiment 2 and the EE group in
72
Experiment 1 suggests that state anxiety may increase as a consequence of performance criteria, we
cannot fully rule out the possibility that state anxiety in the EE group may have already been high at the
beginning of acquisition, compared to other groups. Third, a customized questionnaire was used in this
study. Although a similar customized questionnaire has been used in previous research (Chiviacowsky,
Wulf, Lewthwaite, et al., 2012; Lewthwaite & Wulf, 2010; Wulf et al., 2012), it is not a validated and
standardized questionnaire. Thus, the findings should be interpreted with caution. Finally, although the
participants were not aware of the study purpose and the groups, the experimenter was not blinded to
participants’ group assignment. Although the experimenter was consistent in the interaction with
participants, we cannot fully rule out the possibility that differences in interaction, facial expression and
tone may contribute to the findings observed.
Conclusions
This study demonstrated that provision of performance criteria was the main source of increasing
state anxiety, which was a common response in both individuals with and without PD. Without the impact
of state anxiety, participants with PD whose self-efficacy was enhanced early at acquisition showed
superior task performance at retention than their counterparts, suggesting that self-efficacy can be
modulated to enhance motor learning in individuals with PD. The finding also confirmed the hypothesis
that increased anxiety may have attenuated the beneficial effect of enhanced self-efficacy.
73
CHAPTER 4
Summary and General Discussion
The overall goal of this dissertation was to investigate the impact of efficacy expectation on
motor performance and learning in individuals with PD. Although efficacy expectation has been
demonstrated as an important factor that modifies motor performance and learning in non-disabled young
and older adults (Wulf & Lewthwaite, 2016), whether a direct causal relation between efficacy
expectation and motor performance exists in individuals with PD is not clear.
To answer the question, we started with a focused review and perspective paper (Chapter 2) to
explore the potential contribution of efficacy expectation on motor performance in PD from existing
literature. In the perspective paper, we proposed that a mismatch may exist between motor capability and
motor performance in individuals with PD, and the mismatch may be elicited by differences in efficacy
expectation. In the literature, individuals with PD tended to show a decrement in motor performance
compared with non-disabled older adults, especially when task instructions were self-related and
determined by an individual’s perceived capability (e.g. “move as fast as you can”). In contrast, when the
task instructions were not self-related, performance deficiency in PD was less. We speculated that self-
related task instructions may evoke reduced efficacy expectation in individuals with PD (e.g. “I may not
be able to move fast because of PD”), thereby interfering with motor performance and leading to further
performance deterioration. Decreased efficacy expectation may be more than a simple byproduct of
declined motor capability resulting from pure neuropathology, but may partially contribute to poor motor
performance in and of itself. Hence, efficacy expectation enhancement may be a plausible way to improve
motor performance in individuals with PD. To investigate whether efficacy expectation enhancement
would benefit motor performance in PD, an empirical study was conducted (Chapter 3). In the study, a
motor learning paradigm was utilized to determine the short-term (i.e. acquisition) and long-term (i.e.
retention) impact of increased efficacy expectation on motor performance when individuals with PD
acquired a motor skill.
74
This chapter begins by summarizing the main findings of the empirical study (Chapter 3). Based
on the previous literature and the findings from this dissertation, the effect of efficacy expectation and
state anxiety on motor performance and learning in individuals with PD will be discussed. Next we will
discuss the potential clinical implications. Lastly, limitations of the present work and directions for future
studies will be addressed.
Summary of Main Results
Experiment 1 investigated (1) whether self-efficacy in individuals with PD could be enhanced by
a verbal Enhanced Expectancy statement and (2) whether enhanced self-efficacy would improve motor
performance and learning in PD. We hypothesized that (1) self-efficacy would increase after the
statement, and (2) motor performance and learning would improve with enhanced self-efficacy in
individuals with PD. The findings in Experiment 1 supported part of the hypotheses. Self-efficacy
increased immediately after participants with PD received the Enhanced Expectancy statement,
specifically their confidence in achieving the most difficult performance level, which was consistent with
our first hypothesis. However, motor performance in the group with enhanced self-efficacy did not differ
from the control group at acquisition and retention, which did not support the second assumption.
Interestingly, the enhanced expectancy group reported a high state anxiety compared to the control group.
The findings in Experiment 1 led to two possible explanations. The first explanation is that enhanced self-
efficacy may not have any influence on motor performance and learning when individuals with PD
practice a challenging dynamic balance task. The second explanation is that enhanced self-efficacy and
increased state anxiety may have a positive and negative impact on performance respectively, with
increased state anxiety counteracting enhanced self-efficacy. In order to test which of the above
explanations is more likely, Experiment 2 was conducted. We recruited an additional group of PD
participants, whose self-efficacy was enhanced without inducing state anxiety, and compared their motor
performance with the control group in Experiment 1. The findings from Experiment 2 indicated that there
was a trend that enhancing self-efficacy improved motor learning in individuals with PD, which
supported the second explanation.
75
Impact of self-efficacy on motor performance and learning in PD
The beneficial impact of boosted self-efficacy was manifested mainly on motor performance at
retention, rather than at acquisition, suggesting that a period of consolidation may be necessary for the
impact of self-efficacy enhancement to be demonstrated. The finding is consistent with the evidence in
non-disabled adults (Wulf & Lewthwaite, 2016). The result may reveal the potential timeline for the
Enhanced Expectancy statement to be internalized and cultivate participants’ self-efficacy. It may also
represent the role that dopamine plays in memory consolidation and promoting synaptic plasticity in the
motor cortex for motor learning (Hosp, Pekanovic, Rioult-Pedotti, & Luft, 2011; Hosp & Luft, 2013). As
stated above, the reason why we only observed a trend may be due to the disrupted dopaminergic system
in individuals with PD. Despite the fact that participants with PD in this study were medicated, a larger
magnitude in self-efficacy enhancement may be required to induce improvement in motor performance
and learning. It is also not clear whether there is a threshold with regards to the self-efficacy change that
is necessary in order to induce changes in motor performance. Perhaps several “doses” of self-efficacy
enhancement as an adjunct therapy would be necessary for it to effectively improve rehabilitation.
Impact of state anxiety on motor performance and learning in PD
A correlation between state anxiety and fluctuations of motor performance has been reported in
PD (Siemers, Shekhar, Quaid, & Dickson, 1993). Not until recently, studies have started to demonstrate
that inducing state anxiety related to fear of falling can degrade motor performance in PD, as indicated by
a slowed and variable gait, as well as increased freezing episodes (Caetano, Gobbi, Sánchez-Arias, Stella,
& Gobbi, 2009; Doan et al., 2010, 2013, Ehgoetz Martens, Ellard, & Almeida, 2014, 2015). Our finding
indicates that increased state anxiety may degrade the positive impact of enhanced self-efficacy on motor
learning in PD, suggesting that state anxiety may not only negatively impact performance, but also
learning. Recall that the nervousness induced in this experiment is not the same as anxiety associated with
PD, given that in our study with non-disabled older adults, the EE group reported similar nervousness
level. It has been hypothesized that cognitive anxiety (i.e. the mental manifestations of anxiety, or the
76
specific thought processes that occur during anxiety, such as concern or worry) and somatic anxiety (i.e.
physical symptoms of anxiety, such as butterflies in the stomach) have a negative linear relationship and a
quadratic (inverted-U shaped) relationship with performance respectively in non-disabled adults
(Woodman & Hardy, 2003; Hordacre, Immink, Ridding, & Hillier, 2016). We cannot distinguish whether
the increased state anxiety reported in The EE group is more related to cognitive anxiety or somatic
anxiety. Future studies are required to investigate whether similar hypothetical relations between two
types of anxiety and motor performance will be observed in individuals with PD as in non-disabled adults.
Clinical Implications
The findings from this dissertation suggest that clinicians should be aware of the potential impact
of psychological factors on motor learning in individuals with PD (Zemankova, Lungu, & Bares, 2016).
While non-motor symptoms in PD are starting to be recognized as contributing factors to PD impairments,
therapists largely deal with the movement dysfunction associated with PD. This dissertation has provided
evidence that a component of that movement dysfunction may be low self-efficacy. Boosting self-efficacy
and/or alleviating anxiety in people with PD may represent an effective strategy in improving motor
behavior.
Future directions
Future studies can consider including physiological measures of anxiety, such as skin conduction
rate, heart rate or pupil dilation, as validated measurements for anxiety and to distinguish whether the
increased state anxiety is more related to cognitive anxiety or somatic anxiety. Increased arousal related to
somatic anxiety during a motor task may help improve performance, while cognitive anxiety, such as
concern and worry, may distract attention and be detrimental to performance. A control group who
receives only performance criteria will be needed in order to further confirm whether the impact of self-
efficacy and state anxiety is independent, or whether self-efficacy and state anxiety may have an
interacting effect. Namely, it is not clear whether increased state anxiety alone will interfere with motor
performance and learning in PD, or whether the negative impact of anxiety will appear only when an
individual’s self-efficacy is enhanced. Determining how cognitive processes (such as attentional demand)
77
and neurophysiologic processes (such as modification of dopamine release) may change with enhanced
self-efficacy, will be required to reveal the underlying mechanisms of the beneficial impact of self-
efficacy on motor learning. For example, if the attentional demand of a task is diminished with self-
efficacy enhancement, a patient who is unable to learn a complex motor task due to high attentional
demand may benefit from self-efficacy enhancement. Similarly, if self-efficacy enhancement generates
sufficient dopamine release in one participant versus another, it may reveal greater potential for learning.
The information will help to identify the individuals who may benefit most from self-efficacy
enhancement to optimize therapeutic application in the clinical setting. Additionally, it is also not clear
whether specific individual characteristics can be used to predict a person’s response to self-efficacy
enhancement. Finally, whether the beneficial impact of enhanced self-efficacy will persist for a longer
time beyond one day must also be assessed. Future studies are also required to investigate whether the
findings can be generalized to individuals with advanced disease stage (moderate or severe disease
severity).
78
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Abstract (if available)
Abstract
Efficacy expectation serves as an important factor that modifies motor performance and learning in non-disabled adults. Little is known about whether efficacy expectation may also have an influence on motor performance in individuals with Parkinson’s disease (PD). To answer the question, we investigated whether efficacy expectation enhancement would benefit motor performance in PD. A motor learning paradigm was utilized to determine the short-term (i.e. acquisition) and long-term (i.e. retention) impact of increased efficacy expectation on motor performance when individuals with PD acquired a motor skill. There was a trend that enhancing efficacy expectation improved motor learning in individuals with PD. In addition, increased state anxiety may have a negative impact on motor performance and counteract the positive influence of enhanced efficacy expectation. The findings provide evidence that a component of that movement dysfunction may be related to efficacy expectation. Boosting efficacy expectation and/or alleviating anxiety in people with PD may represent an effective strategy in improving motor behavior.
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Asset Metadata
Creator
Chung, Yu-Chen
(author)
Core Title
Impact of enhanced efficacy expectation on motor learning in individuals with Parkinson’s disease
School
School of Dentistry
Degree
Doctor of Philosophy
Degree Program
Biokinesiology
Publication Date
07/31/2017
Defense Date
05/10/2017
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
efficacy expectation,motor learning,OAI-PMH Harvest,Parkinson's disease,self-efficacy
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Fisher, Beth (
committee chair
), Finley, James (
committee member
), Lewthwaite, Rebecca (
committee member
), Monterosso, John (
committee member
), Winstein, Carolee (
committee member
)
Creator Email
ycchung39@gmail.com,yuchenc@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c40-419408
Unique identifier
UC11264142
Identifier
etd-ChungYuChe-5658.pdf (filename),usctheses-c40-419408 (legacy record id)
Legacy Identifier
etd-ChungYuChe-5658.pdf
Dmrecord
419408
Document Type
Dissertation
Rights
Chung, Yu-Chen
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Tags
efficacy expectation
motor learning
Parkinson's disease
self-efficacy