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Electronic Theses, Treatises and Dissertations
The Graduate School
2008
The Effects of Cognitive Load on Gait in
Older Adults
Kimberly R. Wilson
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FLORIDA STATE UNIVERSITY
COLLEGE OF COMMUNICATION
THE EFFECTS OF COGNITIVE LOAD ON GAIT IN OLDER ADULTS
By
KIMBERLY R. WILSON
A Dissertation submitted to the
Department of Communication Disorders
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Spring Semester, 2008
The members of the Committee approve the Dissertation of Kimberly R. Wilson
defended on March 3, 2008.
Julie A.G. Stierwalt
Professor Directing Dissertation
Rolf A. Zwaan
Outside Committee Member
Leonard L. LaPointe
Committee Member
Joanne Lasker
Committee Member
Lisa Scott
Committee Member
Approved:
Juliann Woods, PhD, Chair, Department of Communication Disorders
John K. Mayo, Dean, College of Communication
The Office of Graduate Studies has verified and approved the above named
committee members.
ii
ACKNOWLEDGEMENTS
I would like to express the deepest appreciation to my committee chair and mentor, Dr.
Julie A.G. Stierwalt, who patiently guided me through the writing of this dissertation. She has
been a wonderful source of encouragement and an ideal model of the professional I aspire to
become.
It was my pleasure and my privilege to work with my other committee members, Drs.
Leonard L. LaPointe, Lisa Scott, Joanne Lasker, and Rolf A. Zwaan. Their contributions were
invaluable and significant in the development of my early career as well as the completion of this
dissertation. I would especially like to thank Dr. Scott for her brilliant advice on proper coding
and analysis of discourse.
I gratefully acknowledge the support of the Neuro-Cognitive Neuro-Linguistic Research
Rehabilitation Center and Tallahassee Memorial Hospital. This project could not have been
completed without the partnership and financial support of these organizations.
The constant love and support of my family is immeasurable and cherished. Mom and
Dad thank you for the prayers, long distance “hugs, and welcoming your prodigal child home
with open arms. Brittany, your perpetual advice to “Suck it up” in combination with the welltimed theme song helped me through many hard days.
I thank all of my friends that I have met in my years at Florida State University. The
downtimes filled with laughter, tears, and the occasional adventure will be treasured always with
the hope of many more to come.
Finally, to my Lord and Savior, Jesus Christ, all glory to Him who makes all things
possible.
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TABLE OF CONTENTS
List of Tables
List of Figures
Abstract
vi
vii
viii
1. INTRODUCTION
1
Theoretical Basis of Multitasking
The Nature of Discourse
Models of Discourse Production
Age Effects in Dual-Task Performance
Summary
1
4
5
8
12
2. METHOD
14
Participants
Instrumentation
Cognitive Load
Low Load
High Load
Procedure
Dependent Measures
Linguistic Complexity
Fluency
Gait Measures
Analyses
Descriptive Statistics
Inferential Statistics
Reliability
14
16
16
16
17
17
18
18
19
20
21
21
21
22
3. RESULTS
23
23
23
23
24
24
24
24
25
25
25
Reliability
Linguistic Complexity
Descriptive Statistics
Inferential Statistics
Fluency Measures
Descriptive Statistics
Inferential Statistics
Gait Parameters
Descriptive Statistics
Inferential Statistics
4. DISCUSSION
27
iv
Influences of Cognitive-Linguistic Load on Linguistic Complexity Measures
Influences of Cognitive-Linguistic Load on Fluency Measures
Influences of Cognitive-Linguistic Load on Gait Measures
Clinical Implications
Future Directions
Summary
27
29
31
33
35
35
REFERENCES
57
BIOGRAPHICAL SKETCH
65
v
LIST OF TABLES
Table 1. Eight Criteria for Classification as Multitasking adapted from
Burgess (2000)
38
Table 2. Discourse Tasks for Low and High Cognitive-linguistic Load
39
Table 3. Situational Discourse Semantic (SDS) Model Adapted from Norris
& Hoffman (1993)
40
Table 4. Means and Standard Deviations of Participants’ Age, MoCA scores,
Days of Physical Activity, and Minutes of Physical Activity
41
Table 5. Means and Standard Deviations of Total Number of Procedural Steps
and Total Number of T-units for Low and High Cognitive-Linguistic Load
Levels Across Baseline and Dual Task Conditions
47
Table 6. Means and Standard Deviations for Proportion of Disfluencies across
Baseline and Dual-task Conditions
50
Table 7. Means and Standard Deviations of Double Support Base (DSB),
Stride Length (SL), and Functional Ambulation Profile (FAP) Across
BaselineG, Low-load, and High-load Conditions
52
Table 8. Example of Participant Responses across Baseline, Low-load, and
High-load Conditions
56
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LIST OF FIGURES
Figure 1. Model of Resource Allocation in Attention (Kahneman, 1973)
37
Figure 2. Mean Minutes of Physical Activity for Participants who Reported Routine
Engagement in Physical Activity
42
Figure 3. Education Level for Participants
43
Figure 4. Internal Review Board Letter of Approval
44
Figure 5. Informed Consent Form
45
Figure 6. Mean Number of Procedural Steps across Baseline and Low-load Conditions
48
Figure 7. Mean Number of T-units across Baseline and High-load conditions
49
Figure 8. Mean Proportion of Dysfluency across Baseline, Low-load, and
High-load Conditions
51
Figure 9. Double Support Base for BaselineG, Low-load, and High-load Conditions
53
Figure 10. Stride Length Differences for BaselineG, Low-load, and High-load
Conditions
54
Figure 11. Functional Ambulation Profile (FAP) for BaselineG, Low-load, and High-load
Conditions
55
vii
ABSTRACT
It is a well-documented fact that the rate of falls increases with advanced age. In an
attempt to discern factors that contribute to the high fall rate in aging populations; investigators
have looked to a task that is often executed while walking – talking. Although the effects of
varying cognitive-linguistic load across a variety of dual-tasks have been heavily researched, few
studies have systematically examined the contribution of increasingly complex cognitivelinguistic load on the gait parameters of healthy aging adults. Moreover, few researchers have
utilized ecologically valid stimuli as a part of their investigation. The broad goal of this research
was to examine the nature of the impact of manipulating cognitive-linguistic load hierarchy on
gait in healthy aging. Discourse tasks of varying complexity were presented while participants
walked a 44-foot walkway. The progression of discourse was from low load (explaining how to
perform a task) to high load (completing a story initiated by the investigator). The dependent
measures collected included measures of linguistic complexity, fluency, and gait.
Results showed a significant effect of cognitive-linguistic load linguistic complexity on
measures of discourse. Comparison of fluency measures across conditions revealed that
cognitive-linguistic load did not have a significant impact on fluency. However, like measures of
discourse, gait parameters were significantly affected by the addition of a secondary cognitivelinguistic task. Theoretically, these results could imply that a change in cognitive-linguistic and
gait measures is linked to a sharing of resources involved in the execution of both tasks. In
addition, these results also provide insight into dual-task performance and the potential
contribution of cognitive-linguistic load on fall rates in healthy and neurologically compromised
populations.
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CHAPTER 1
INTRODUCTION
Multitasking is the process of performing two or more tasks simultaneously within the
same time frame (Law, Logie, Pearson, Centagallo, Moretti, & Dimarco, 2004). The increased
complexity of modern culture renders multitasking as a necessary skill. As such, researchers
have begun to investigate multitasking in controlled experiments to identify and gain
understanding of the underlying cognitive functions that allow the execution of multiple tasks in
what might appear to be an apparently effortless manner.
Theoretical Basis of Multitasking
There are numerous cognitive processes involved in the execution of a task. Yet, for the
task to be accurately and adequately completed, the focused attention of the individual is needed.
When a person performs several tasks within the same time frame, attention may be divided
between the various jobs (Wickens, 1991). Since time sharing among resources is common in a
multitasking society, theories developed on this subject are not focused on whether individuals
are capable of multitasking, but rather how an individual is able to divide attentional resources
across multiple and varied tasks. The most widely accepted explanation for processing resources
and attention is the idea of resource allocation (Wickens, 1991).
Kahneman proposed the theory of resource allocation in 1973 in an attempt to explain the
relationship between task demands and attention known as performance resource function (PRF).
A resource was defined as the amount of effort allotted to a given task. Figure 1 displays a model
of resource allocation as explained by PRF. According to Kahneman (1973), more resources are
allocated to task performance as task demands increase. In other words, the harder the task, the
more resources needed to accurately complete it. Usually, task performance improves as effort
increases. The effort exerted on a task is momentary and associated with the structure of the task
itself (Kahneman, 1973). For example, if time pressure is present during execution of a task, that
demand places an extra load on the person’s short-term memory. Short-term memory allows an
individual to keep track of tasks’ goals and the steps already taken to reach that goal. Thus, time
pressure places constraints on how long a person can process the inherent structure of a task in
short-term memory (Kahneman, 1973). With the addition of a second task, greater stress is
placed on the entire system. The same resources allocated to completion of the first task, must be
1
allotted to a second task. Therefore, depending upon task difficulty two tasks might result in
limited availability of resources for one or both tasks. Consequently, a number of effects might
be observed. For instance, the second task may not be carried out as efficiently as the first task,
the first task may not be carried out as efficiently as the second task, or performance on both
might suffer. Numerous studies have shown that the extent to which resource allocation affects
task performance varies according to the type of task and effort (Kanfer, Ackerman, Murtha,
Dugdale, & Nelson, 1994; Bourke, Duncan, & Nimmo-Smith, 1996; Pashler & Johnston, 1998;
Patten, Kirchen, Östlund, & Nilsson, 2004).
As a rule, more complex tasks will cause more interference than simple tasks (Bourke,
Duncan, & Nimmo-Smith, 1996; Pashler & Johnston, 1998). This may be related to the fact that
goals and strategies of complex tasks are more complicated and may require more cognitive
demand for accurate completion. With respect to multitasking, if the primary task is simple and
the secondary task is complex, performance on the primary task may suffer as a result of an
allocation of available resources to the more complex secondary task. This phenomenon is
known as task interference. Kahneman (1973) discussed task interference in terms of capacity
and structural limitations. According to the original proposal of resource allocation, attentional
capacity was defined as the capacity for attention human beings posses. While attending to one
task may be relatively easy, attending to two or more tasks can overload the system causing a
decrement in task performance. For instance, conversing with a friend may not tax attentional
resources, but completing a mathematical equation while conversing with a friend may cause
decrement in performing one or both of the tasks due to the weight placed on the allotted
reserves with the addition of a second task (Chun & Potter, 2001; Kahenman, 1973). Moreover,
in the example above, the primary task (conversing) may be considered simple while the
secondary task (math equation) may be considered more complex. These differences demonstrate
another factor that may cause task interference - the similarity of the secondary task to the
primary task.
The amount of attention given to a task is dictated by the task itself, but allocated through
the central processing mechanism of the person’s cognitive system (Bourke, 1997; Logan &
Gordon, 2001). When more than one task competes for attention, a bottleneck can occur in the
central processor restricting perceptual processing of the tasks. Structural, or task interference
occurs when task demands compete for the same processes simultaneously (Kahneman, 1973).
2
Law, Logie, Pearson, Cantagallo, Moretti, and Dimarco (2004) also found that the more similar
two tasks are to one another, the less significant the effect of task interference due to shared
perceptual mechanisms. For example, viewing sentences on a monitor and vocalizing responses
would not cause interference due to the differences in the modalities for the task. However,
viewing sentences and visualizing responses by pointing to sites on the screen has been found to
cause task performance to decrease (Law, Logie, Pearson, Cantagallo, Moretti, & Dimarco,
2004). Decreased performance in the second scenario is thought to be due to competition for
visual resources (e.g. viewing the sentences interferes with accurately visualizing sites for
pointing), whereas improved performance in the first scenario can be attributed to the allotment
of full visual and auditory resources to the task. In other words, the lack of competition for
resources facilitated increased accuracy in task performance in the first task versus the second
task (Kramer, Wickens, & Donchin, 1985; Wickens, 1991). These demands may also vary
within as well as between tasks. Within a single task, differences in input and output modalities
can cause competition for attentional resources. The addition of a second task, which may also
presents a conflict between modalities, can further add to the competition for attentional
resources. There are many combinations of complexity level that can arise with multiple tasks,
with each task increasing the competition for resources due to the structural interference both
within tasks and between tasks. Limitations on perceptual processing may hinder reaction time
and accuracy, which may cause a decrement in task performance, as a result of divided attention
(Pashler & Johnston, 1998).
Although this explanation of task performance appears anecdotal, there are several issues
that the theory of allocated resources does not address. First, the criteria for identifying resources
are not clear. Loosely defined, a resource is a subset of cognitive processes that is associated
with the physiological structure of a specific task (Wickens, 1991). Critics of the resource
allocation model argue that the dimensions of resources can be expanded depending on the
situation or the model. Proponents of resource allocation caution theorists to abide by the criteria
outlined by Kahneman (1973). These criteria include the definition of a resource and the
presence of a given capacity for completion of any task, which is the second issue researchers
have against the theory of allocated resources. The existence of a single, general capacity for task
execution has not been proven (Bourke, Duncan, & Nimmo-Smith, 1996). Researchers in the
field of cognitive psychology question whether task interference is due to misallocation of
3
resources or the inability to recruit additional resources. More empirical data are needed to
determine the existence of undifferentiated resource capacity as well as the cause of task
interference. Moreover, many investigators believe that the cognitive skills utilized during daily
multitasking are different from the attentional resources used in laboratory tests of dual-tasks
(Burgess, Veirch, de Lacy Costello, & Shallice, 2000; Herath, Klingberg, Young, Amunts, &
Roland, 2001).
During daily multitasking, linguistic tasks are often overlaid on other everyday duties. In
observing these everyday activities, individuals can be seen executing such tasks as eating,
driving, and walking while conversing with other people. As discussed in the section above, the
accuracy with which two or more chores are executed is dependent on many factors including
the complexity of each task and successful allocation of cognitive resources to the multiple tasks
to be completed. For example, conversing while walking is a fairly common daily multitask that
appears to be carried out with few complications. Yet, the precision of one’s speech, the clarity
of one’s ideas, and/or the steadiness of one’s gait may be affected by the complexity of the
conversational task as well as the effectual and simultaneous allocation of resources needed to
attend to each aspect of the dual task. In neurologically intact individuals, walking can be
perceived as a relatively automatic action, but the amount of attention required to converse with
someone is dependent on the type of discourse elicited.
The Nature of Discourse
During social interaction, cognitive and linguistic information is shared by means of
communication. Communication is defined as the process by which persons share ideas, desires,
needs, and information (Owens, 2001). This exchange can occur through linguistic (e.g. spoken
or written word) or nonlinguistic (e.g. gesture or prosody) means. The most common means of
communication is through oral or written discourse.
Discourse can be defined as a “set of propositions related to one another so as to produce
a sense of cohesion for a listener or reader” (Jenkins, Mishel, & Hartup, 1984). The sender
modifies the form of discourse according to the basic intent of the communication. Discourse
genres include but are not limited to conversational, descriptive, procedural, and narrative.
Identification of the type of discourse occurring in any given communicative exchange is
important for the organization of thoughts and subsequent words used to convey those thoughts
by the sender. Moreover, to avoid miscommunication, comprehension of the message by the
4
receiver relies on accurate recognition of the genre. Nonlinguistic forms of communication are
also influenced by genre. In any given conversation a person may modify facial expressions,
prosody of the voice, or body gestures in addition to speaking words to convey a message.
Effective oral communication relies on the efficient integration of linguistic and nonlinguistic
forms for accurate perception and production of shared messages between communicators.
Quantifying the effectiveness of a communicative exchange is problematic due to the
endless combinations of genres, topics, and individual communication styles that exist within the
social contexts of discourse. The spontaneous and random nature of conversing in social contexts
creates a challenging environment for analyzing discourse. In an attempt to explain spontaneous
communication, a variety of theories and models have been proposed that account for the
complexity of human discourse. Proposed theories exist to explain two fundamental aspects of
discourse, comprehension and production. Since the focus of the current study has to do with the
production of oral discourse, only theoretical models for discourse production will be addressed.
Models of Discourse Production
While there are theories that address discourse comprehension and production,
production has not been studied as extensively as discourse comprehension. One reason may be
the unpredictable nature of discourse production that makes it difficult to control in experimental
settings. As such, it is most easily examined in the written form versus the spoken form.
However, characteristics of the written form mirror the primary levels of sequencing in spoken
discourse (Allport, MacKay, Prinz, & Scheerer, 1987). From the study of written discourse
production, models have been proposed as a means to provide insight into the spoken forms. One
such model draws from key features of connectionist theories (Levelt, 1989).
The process involved in the construction of sentences can be complex but proper
selection is crucial to communicating concepts effectively. In the connectionist model of
discourse production, spoken language relies on the correct selection of words from distributed
networks. This selection process can be divided into two basic phases known as the lexical and
grammatical phases. During the lexical phase, the speaker’s vocabulary is activated by the idea
or concept to be conveyed. It is hypothesized that within the lexical phase there are two steps.
The first step involves deciding on the non-phonological representation, or the lemma, of the
word that expresses the most accurate sense of the concept. Next, the phonological form of the
word is chosen from the network. The phonological form functions as a plan for sequencing the
5
speech sounds included in the word. After the lexical phase is complete, the grammatical phase
begins (Dell, Chang, & Griffin, 1999). The grammatical form provides the structure from which
the meaning of the sentences or phrases is derived (Jenkins, Mischel, & Hartup, 1984).
Although concepts convey meaning, in order to express the intended meaning of the speaker,
sentences must be carefully constructed to include these dimensions.
Psycholinguists have recently begun to focus analysis on how the structure of spoken
discourse is constructed rather than on the structure itself. Well-structured written discourse
forms a hierarchy during comprehension of the content. This statement is not as easily applied to
oral discourse due to the spontaneous nature of conversation. Yet the hierarchy of any given
discourse is not necessarily the correct outline; it is just the hierarchy that formed based on the
structure of the discourse and to a minimal extent, the reader’s or listener’s interpretation
(Jenkins, Mischel, & Hartup, 1984). In other words, the comprehension outline constructed by
the receiver of any discourse may not be the outline originally intended by the narrative’s author.
It is just the outline formed based on how the narrative was structured as well as the receiver’s
prior experience with the subject matter. Interpretation is based on knowledge of the speaker and
topic as well as the lexical knowledge of the listener. One model of discourse production that
addresses the formation of hierarchical structures prior to comprehension is the hierarchical
coding model (Allport, MacKay, Prinz, & Scheerer, 1987).
Whereas connectionist models of discourse assume a spreading activation of processing,
the hierarchical coding models are based on top down processing. In other words, the higherlevel units within the network dictate how lower level units are represented. The hierarchical
coding model attempts to explain the flexibility of motor acts during conversation. Despite its
limitations, this model supports the notion that perceptual and production aspects of discourse
share common processes due to the fact that in conversations, a listener’s response sequence
depends on the complexity of the discourse produced by the speaker (Allport, MacKay, Prinz, &
Scheerer, 1987). For instance, a more abstract, complex utterance may activate higher-level units
than a more concrete phrase, which only requires activation of lower units. The priming of the
various levels can occur either within a single central idea or between central ideas during the
course of a conversation. This model is highly theoretical and only accounts for the complexity
of the speaker’s utterance instead of the complexity of the utterance within a given situation. A
6
hierarchical model that considers both the utterances to be produced and the situation within
which they are uttered is the situational discourse semantic (SDS) model.
As a means for analyzing the complexity of discourse along three dimensions of context,
Norris and Hoffman (1993) created the SDS model. The first dimension, situational context, is
divided into ten different levels that span two broad categories, contextualized and
decontextualized. Within the contextualized category, the levels range from sensorimotor
stimulation of the speaker’s own body to recounting a personal experience. The common theme
in contextualized situations is that the situation is occurring presently. By contrast,
decontextualized situations have to be re-created through the use of language. The range of
complexity within this category begins with the recreation of a personal event and ends with
manipulation of abstract concepts to communicate a situation. The second dimension is the
discourse context. Again, discourse is divided into two categories – transactional and poetic
function. Transactional function is primarily for descriptive purposes whereas poetic function is
narrative in nature. Like situational context, discourse context is parceled into a hierarchy
extending from random expression of ideas with little or no structure to highly structured
dialogue integrating multiple protagonists with multiple intentions. The third dimension,
semantic context, is also divided into ten different levels. The lowest levels involve direct
reference with concrete meaning while the highest levels are metalinguistic in nature. Semantic
contexts are also split into experiential (words/phrases used from personal experience) and
erudite (words/phrases from knowledge of the world, culture, and history). This model has been
useful in evaluating the discourse patterns of children with typically developing language skills
as well as for assessing and treating the disordered discourse patterns of children with
communication impairments.
The nature of discourse is often discussed in isolation. However, many of the daily
activities which include discourse occur in a multitask, or dual task format. To refer to an earlier
example, these multiple tasks may include monitoring a boiling pot during conversation or
conversing with a friend while walking. In the last example, the cognitive-linguistic load of
discourse coupled with motoric demands of walking may leave these individuals susceptible to
falls especially when the cognitive and motoric theories of each dual task are considered.
Certainly, as outlined by the SDS model, there are hierarchical levels of complexity when
discourse is considered, so if different levels of discourse are employed, that will inherently
7
change the demands of the task. Research has shown that fall rates are high in elderly
populations over the age of 65 (Daal & Lieshout, 2005). This research has also implicated many
factors as contributing to an increase in fall rates among aging adults including the execution of
another task while walking. One task that is commonly executed while walking is the production
of discourse.
Age Effects in Dual-Task Performance
Increasing healthcare costs concomitant with the rise in the population of aging have led
to several investigations to determine what age-related physical changes contribute to falls that
might be associated with of completing another task while walking. Results of these studies
show that growing costs related to falls in aging adults can be attributed to many factors
including sacropenia, or age-related changes in skeletal musculature. Sacropenia is responsible
for 90% of observed decreases in muscle strength in the aging although the process begins after
the age of 24 (Deschess, 2004; Larsson & Ramamurthy, 2000). Before the age of 50, few people
notice the subtle breakdown of muscle cells and fiber. After 50, the rate of sacropenia
accelerates, particularly during the years shortly before death (Daal & Lieshout, 2005),
significantly affecting quality of life for aging adults. One major consequence of sacropenia is
the inability to prevent falls due to weakness, reduction in reflexive movements, and general loss
of posture.
Fall rates and related injuries such as fractures are high in older adults. Many factors have
been faulted for the increase in fall rates. One factor is the influences of medication on neural
and musculature resources. Some medications have been found to cause dizziness that can lead
to impairments in balance and posture, two crucial aspects of gait. Studies have also found a
linear relationship between the number of medications taken and incidence of falls and more
medications equal greater likelihood of sustaining a fall (Daal & Lieshout, 2005). Generalizing
conclusions from these studies is problematic due to cohort effects that may cloud true agerelated changes in gait. Age related changes in gait have also been investigated as a possible
culprit in increasing fall rates in aging populations (Verghese, Lipton, Hall, Kuslansky, Katz, &
Buschke, 2002). It has been found that younger adults tend to have faster gait speeds than older
adults. Further, older adults lack the ability to compensate for postural changes while standing
and time spent in single leg stance (narrowed base of support) is greatly reduced by the age of 80
8
(Wolfson, 2001) indicating that there is a gradual decline of basic balance functions that underlie
gait ability. Neural processes control these underlying functions.
Age-related neural and cognitive changes have also been implicated as the cause of agerelated gait changes observed in aging adults. Cognitive changes have been demonstrated in the
study of the effects of age on the formation situational models. Zwaan and Radvansky (1998)
define situation models as conditions that are formulated from the context represented in the text
an individual reads. An example of a situation model would be the representation of a specific
trip to the grocery store (e.g. “last Friday we shopped at the Publix on Ocala Road”) (Zwaan &
Radvansky, 1998). Once created, the situation model is stored in the working memory (WM) of
the individual and updated as more information is added during the course of reading or listening
to any given discourse. While the creation of situation models changes minimally with age, older
adults have been observed to spend more time on comprehension and memory of textually
presented information than younger adults (Radvansky, Copeland, & Zwaan, 2003; Radvansky,
Zwaan, Curiel, & Copeland, 2001). Implications of this observation may be that processes
related to discourse are more cognitively demanding in older populations. This finding is
consistent with theories of cognitive aging.
One theory focuses on the prefrontal cortex, which has been implicated in attention. The
theory hypothesizes that with age, the capacity for attention is depleted which leads to deficits in
prospective memory, retrospective memory, interference control, and inhibition of response
(West, 1996). Cognitive decline has been observed in behavioral studies as well as neuroimaging
studies. Imaging studies have confirmed a loss of neural matter with age but subsequent
behavioral changes are difficult to discern. Yet, results of cross-sectional and longitudinal studies
of life-long declines have shown a steady decrease in processing speed, working memory, and
episodic memory (Hedden & Gabrieli, 2004). These studies have also shown that well practiced
tasks are not impacted until late in life. Important clinical implications can be assumed from
these conclusions. First, cognitive resources appear to be important to the efficiency of motoric
actions. Cognitive skills such as motor planning are crucial to motor execution and rely on intact
working memory and the ability to sustain attention (Woollacott & Shumway-Cook, 2002).
Secondly, well-practiced tasks like walking are often performed in conjunction with cognitive
tasks like talking. Completion of a secondary task while walking is not only a byproduct of a
multitasking society, but has also been found to be beneficial for sharing of ideas, moving of
9
objects or monitoring changes in the environment to avoid danger (O’Shea, Morris, & Iansek,
2002).
In a study that examined gait alterations made while performing complex verbal tasks,
O’Shea, Morris, and Iansek (2002) found that healthy older adults displayed marked gait changes
when the task required more attentional resources. Participants with Parkinson’s disease were
also found to have gait impairments with the addition of verbal tasks; however, the impairments
in this clinical group appeared to be more generalized (Stierwalt, LaPointe, Toole, Maitland, &
Wilson, 2006; LaPointe, Stierwalt, Maitland, Heald, Wilson, Cicchitto, & Apel, 2007). Bowen,
et al (2001) found that the gait of stroke survivors was also impaired when a secondary verbal
task was added. This conclusion has been found with most studies focused on dual-tasks effects
of walking while talking.
The proper use of language requires many of the same cognitive processes that are
necessary for maintenance of balance and posture during gait. Yet, few studies have examined
the linguistic load in a systematic fashion to determine at what level of difficulty the effects were
revealed. Specific tasks such as serial subtraction, verbal fluency, and yes/no response were
utilized in these studies. One way to systematically approach the study of dual-task effects in
walking and talking is to view cognitive-linguistic ability as a hierarchy of skills. Manipulating
discourse in a hierarchical fashion to study effects of narrative production on gait may provide
more insight into the underlying cognitive process responsible for decreased performance in
dual-tasks paradigms. Moreover, investigating discourse in a hierarchical fashion follows with
recent theories of cognitive organization.
Hierarchical organization of cognitive abilities is a relatively new area in the study of
cognition. Researchers like Velichkovsky (2002) have proposed that there are multiple levels
involved in the processing of a task. Each level is built on the concepts of the preceding level.
Levels of processing range from basic detection to integrating knowledge into a metacognitive
perception of the world. Although the assumption is that thoughts and ideas are represented as
concepts, it should be noted that most theories of cognitive hierarchy are built around the
organization of cognition not the representation (Cohen, 2000). This assumption can be clearly
seen in the study of language, a skill that requires the use of cognitive faculties. In the field of
communication disorders, hierarchical theories have been typically used in the assessment of
language skills in children. For example, the Clinical Evaluation of Language Fundamentals-4
10
(CELF-4; Semel, Wiig, & Secord, 2003) uses word pairs that range in degree of relatedness to
assess a child’s ability to formulate grammatically correct sentences, sentences varying in length
to assess the child’s ability to recall information, and clusters of words that include one unrelated
term to assess the child’s categorical and conceptual skills. All of these tasks require cognitive
processes for accurate completion – lexical manipulation, immediate/short term memory, and
working memory, respectively. Further, all of these tasks approach the assessment of cognitive
linguistic skills in a systematic hierarchical fashion. Yet, there is a paucity of literature that
assesses linguistic tasks according to the quantity of cognitive involvement needed for execution.
Given that language involves many cognitive processes, it follows that a hierarchical approach to
cognitive-linguistic analysis can be created based on recent theories of cognition. Furthermore, a
systematic approach to the investigation of cognitive-linguistic interactions may aid in the
improvement of assessment and subsequent treatment of cognitive-linguistic impairments in
individuals who are neurologically compromised.
Although healthy aging adults are at considerable risk for falls, the neurologically
compromised population is at an even greater risk due to the damage to cortical and subcortical
regions of the brain that exacerbate neurological modifications seen in normal aging. Dualtasking abilities have been heavily investigated in this population in recent years in an attempt to
minimize the probability of falls and to obtain a better understanding of cognitive skills in light
of progressive or acute damage to specific cortical and/or subcortical regions. The effects of
walking while talking have been examined in several studies but few have done so
systematically. A recent study by Stierwalt, Toole, LaPointe, and Maitland (2005) methodically
evaluated the effects of three levels of cognitive-linguistic load on gait measures in individuals
with Parkinson’s disease (PD). Participants were instructed to walk the length of a gait mat while
performing low, medium, and high difficulty cognitive-linguistic tasks. The low difficulty tasks
consisted of counting forward beginning at a number specified by the investigator. Medium
difficulty tasks required the participants to begin at a given number and count backwards by
threes. The final cognitive-linguistic task, high difficulty, involved verbal completion of letternumber sequences beginning at a random point in the English alphabet with a randomly chosen
number (i.e. K-11). The preliminary data from this study revealed that individuals with PD
altered their gait when talking as compared to when they are not talking. Further, step length and
velocity progressively shortened and slowed while time measures progressively increased as the
11
level of complexity intensified. These results suggest that not only are gait measures affected by
cognitive-linguistic load, but the degree to which gait parameters changed was related to the
complexity of the secondary task. Though providing insight into the effect of dual-tasking in
persons with PD, a limitation is the use of well-practiced motor tasks (e.g. counting, serial
subtraction) that, while useful for exploration of dual task effects, does not reflect the structure or
cognitive load of conversation during a dual task.
Summary
Multitasking depends on the efficient and accurate integration of cognitive and motoric
skills. Increasing the load in one or both of these areas for any given task may lead to task
deterioration due to the limited capacity for attention in human minds. One multitask that falls
within the constraints of attentional capacity is walking while talking. While conversing with
another individual appears to be a simple task, studies of discourse have shown it to be a multilevel complex act that involves motor and cognitive characteristics for the production and
comprehension of language. More complex statements will activate higher level processing and
subsequent higher-level production than simpler linguistic expressions. As such, this activation
may be impacted by age or the presence of another task like walking.
As previously discussed, isolated age-related gait changes can cause significant
interference in carrying out activities of daily living such as grooming. When age-related gait
changes are combined with paralleled changes in cognition, the result can be detrimental for the
individual. More often than not, the result of these cognitive and motoric changes may be a fall.
Over one third of adults 65 and older sustain a fall each year and of those who fall, 30% require
hospitalization for injuries (National Center for Injury Prevention and Control, 2007).
Hospitalization for fall related injuries result in approximately $20 billion in direct cost to
individuals over 65 annually. This total cost does not include the cost of long term care for
disabilities resulting in lost time from work, inability to complete household duties, or reduced
quality of life. In an effort to examine the increasing rate of falls, a common dual task, walking
while talking, has been the focus of investigations in recent years. Given that the neural networks
that control both walking and talking are overlapping, some researchers have begun to study the
role that cognitive-linguistic load plays in walking while talking. Since cognitive-linguistic
functioning ranges from simple to complex in nature, there is a need to study this process in a
systematic manner.
12
Due to the paucity of knowledge on dual-tasks effects with systematic examination of
discourse skills, the proposed study attempted to create a cognitive-linguistic hierarchy for use in
dual-task paradigms to promote better understanding of cognitive and motoric interactions across
the life span. Moreover, this cognitive-linguistic hierarchy could be used to study gait and
discourse production changes while individuals complete the most basic multitask – walking
while talking. Unlike previous studies that used well-practiced motor tasks, the current
investigation chose discourse production due to the fact that discourse is more ecologically valid
than more automatic tasks like counting. While discourse production is the focus of the current
study, gait measures will also be obtained to observe changes in both tasks under a dual-task
paradigm. It is anticipated that participants’ gait will be significantly altered during execution of
high cognitive-linguistic load as observed by shortened step length, slowed velocity, and
increased travel time. Further, increased time and error in execution of the same high cognitivelinguistic load will be characteristic of speech measures in dual-tasks paradigms. These measures
will be significantly different from the measures of speech obtained while the participants are
seated. It is also anticipated that the lower level of discourse will alter gait or speech measures.
The hypotheses of the current study are:
1. In a dual task paradigm (walking while talking) as level of discourse increases,
participants will show changes on measures of linguistic complexity and fluency.
2. In a dual task paradigm (walking while talking) as level of discourse increases
participants will show changes in parameters of gait.
13
CHAPTER 2
METHOD
Participants
Thirty-six healthy older adults participated in this study. Participants were recruited for
the current investigation through the use of an alumni database supplied by Florida State
University Alumni Association. The age range for participants was 50 years of age and older to
represent the age range most vulnerable for falls (Daal & Lieshout, 2005). All participants were
native speakers of the English language and had a negative history of (a) brain injury or
cerebrovascular accident, (b) hip or knee replacement surgery, (c) impairments affecting ability
to walk, and (d) diagnosis of neurological or muscular degeneration. Each participant completed
a questionnaire, which included questions regarding general health, education, and activity level.
Because the experimental tasks contained varying levels of cognitive demand, it was
important to ensure that our participants were cognitively intact. Therefore, the Montreal
Cognitive Assessment (MoCA) was administered to screen for cognitive or linguistic
impairments for all participants. Similar to the Mini Mental State Exam (MMSE), the MoCA is
scored on a 30-point scale. This short screening tool is comprised of tasks aimed at examining
several cognitive-linguistic areas including visuospatial, naming, immediate and short-term
memory, working memory, and orientation. Individuals who score below a 26 are assumed to
have some impairment in cognitive-linguistic ability. In side-by-side comparison of the
sensitivity and specificity to subtle cognitive changes, the MoCA was found to be a better
assessment of mild cognitive impairment than the MMSE (Nasreddine, Bédirian, Charbonneau,
Whitehead, Collin, Cummings, & Chetkow, 2005). The sensitivity to cognitive-linguistic
impairment and brevity in administration were the determining factors in selecting the MoCA as
the screening tool for potential participants.
MoCA scores ranged from 22 to 30 with a mean of 27.13 indicating that as a group,
participants did not demonstrate overt cognitive impairment (Table 4). Six participants scored
below the cutoff score for unimpaired cognitive function, however, these participants did not
report or exhibit any signs of cognitive impairment. Evaluation of individual MoCA tests showed
that these six participants had difficulty with three major areas of cognition – attention, language,
and memory. The attention related task that posed the most difficulty was serial subtraction. This
14
task required participants to start at 100 and count backward by serially subtracting sevens.
Participants who fell below 26 consistently responded correctly with the first number but were
unable to retain the pattern of sevens for consecutive numbers. These participants also had
difficulty in generating eleven or more words beginning with the letter “F” in 60 seconds. Verbal
fluency tasks like this were aimed at assessing language function. According to the MoCA
manual, producing eleven or more words in minute for a given category indicates normal
linguistic ability. These participants generated a range of 7 – 10 words in the 60 second interval.
The third common area that was problematic for these six participants was delayed recall. As a
means for assessing memory function, participants were verbally given five words to memorize.
After a 5-minute delay, the examiner asked the participant to repeat the five words. For this task,
points are awarded only for uncued recall of the words. However, the examiner is allowed to
give category or multiple-choice cues to aid in recall as well as to assess the level at which the
individual is able to recall information (i.e. abstract categorical cue versus concrete word
recognition in multiple-choice cues). Every participant who scored below the cutoff score of 26
required either a category or multiple-choice cue to recall all five words. In spite of this
difficulty on the MoCA these subjects were active, functioning adults who, though retired, led
active lifestyles.
In addition to screening for cognitive ability, and because the task included ambulation it
was important to ensure that participants were active. To determine their level of activity
participants were asked if they participated in physical activity. If the answer was yes, they were
asked to report how many days per week and how long were their sessions of physical activity.
Examination of Table 4 indicates that the mean number of days and range involved in physical
activity were 3.27 and 1.8, respectively. The number of minutes engaged in physical activity was
divided into four different ranges (see Figure 2). The ranges were broken down as follows: 1 –
10 to 20 minutes per day of physical activity; 2 – 30 to 60 minutes per day of physical activity; 3
– 60 to 90 minutes per day of physical activity; 4 – Two hours or more per day of physical
activity. A mean of 1.8 indicates that participants spent approximately 30 to 60 minutes per
session in physical activity.
Figure 3 illustrates the distribution of education level among the participants. All
participants had a minimum high school education. With regard to degrees, eight participants
15
attained a high school diploma, ten had undergraduate degrees, and the remaining eighteen held
graduate degrees (ten at the Masters level, and eight held doctorates).
Instrumentation
To collect and analyze data related to gait, the GAITRite© system was employed. The
GAITRite© system includes a 14ft walkway containing over 16,000 sensors which are
distributed throughout. These sensors make it possible to record over 30 different parameters of
gait, such as velocity, step length, and base of support measurements. The GAITRite© system
allows for this analysis of gait parameters continuously rather than moment-to-moment as in
other means for examining gait, such as placing sensors on the legs or on a walking aid (e.g.
cane, walker, crutch). Given that the GAITRite© walkway is only 14 feet in length, an additional
15 feet was added both before and at the end of the mat for a total walking path of 44 feet. This
additional length allowed ample time for participants to complete experimental tasks. Gait
parameters were analyzed using the accompanying software included in the GAITRite© system.
Cognitive Load
The cognitive-linguistic tasks were based on the assumptions of hierarchical theories of
discourse specifically those underlying the SDS model of discourse production (see Table 3).
The complexity of the elicited discourse varied from ego-centered (low) to a decentered (high)
level within the situational contexts outlined in the SDS model. All tasks required participants to
produce a narrative both while seated (baseline) and in the time it took to walk the length of the
walkway during experimental tasks. Cognitive-linguistic load levels were counterbalanced across
participants and each was completed twice to account for possible order and learning effects.
Low Load
According to the SDS model, ego-centered discourse relates a personal experience that
may have occurred to an individual. This level of discourse typically involves retelling the
incident in a variety of different ways including episodic (sequencing events in a way that makes
the experience stand out from others), narrative (using multiple characters who perform multiple
actions to create a variety of events), or procedural (presenting a series of steps that lead to a
goal). Since the purpose of procedural discourse is to instruct rather than inform as with episodic
or narrative discourse, the most basic form of ego-centered discourse is procedural discourse
(Wikberg, 1992). As such, the low-load level of discourse was to elicit procedural, or how-to,
dialogue. Participants were asked to describe how to change a tire, how to make a sandwich, how
16
to write a letter, or how to withdraw money from a bank account (Snow, Douglas, & Ponsford,
1997). Procedural discourse would include key components such as the chronological ordering
of steps using connectives like first, then, or last to form a method for reaching a goal.
High Load
The high-load level of discourse comprised completing a decentered narrative, which was
initiated by the examiner. The SDS model defines decentered discourse as discussing an event
that did not happen to the individual personally. For example, retelling a television show,
recapping the local news, or summarizing a movie are all instances of decentered discourse.
Similar to procedural discourse, the events of a narrative are organized chronologically, however
instead of forming a process, the construction of events forms a story for informing the listener.
In the current study, narratives will be elicited through the use of story starters. Story starters
have been found to be effective in prompting narrative discourse with children as well as with
patients with aphasia (Graves, Semmel, & Gerber, 1994; Li, Williams, & Volpe, 1995).
Examples of story initiators are presented in Table 2.
The story starters developed for the current investigation were adapted from Gray’s Silent
Reading Test-3 (GSRT-3; Wiederholt & Blalock, 2000). Passages were selected based on the
capacity to elicit a story. Microsoft Word was used to modify the passages in order to establish a
standard grade level. Grade level was established by the Flesch-Kincaid Grade Level formula,
which is a component of Microsoft Word. The formula incorporates the average sentence length
and the average number of syllables in the document to yield a composite grade level. All
passages were adapted such that each was between a sixth or seventh grade level with grade
levels ranging from 6.2 – 7.4.
Procedure
Institutional Review Boards at Florida State University and Tallahassee Memorial
Hospital approved all experimental procedures, and informed consent was obtained prior to
initiating participation (Figure 4). Participation in the current study required one 30-60 minute
session. Following administration of the MoCA to screen for cognitive ability, baseline measures
were collected. Baseline measures included completion of all dual task components in a single
task format. Establishing baseline performance on all experimental tasks provided data for
comparing the potential deleterious effects that might occur during a dual task. Therefore, each
17
participant completed both low and high-load conditions while seated. In addition, they walked
the 44-foot path without talking to establish baseline measures for gait.
Upon completion of baseline measures participants were asked to complete cognitivelinguistic load tasks in the same order that tasks were presented during baseline. The GAITRite
© System recorded individual data for gait performance and speech measures were coded and
entered into Systematic Analysis of Language Transcripts (SALT; Miller & Chapman, 2000) for
later analysis.
Dependent Measures
Linguistic Complexity
Pilot data for the current study revealed that the nature of the two discourse levels made it
difficult to find a common measure for comparison. The nature of procedural discourse was
more amenable to measurement by the number of steps rather than the number of t-units as with
high-level cognitive tasks. Examination of statements produced in the low cognitive-linguistic
load during the pilot study revealed that if the utterances were collapsed into t-units, valuable
information on the number and type of steps a participant used to reach the desired goal would
be lost. For example, the utterance “And then depending on whether you’ve got a spare tire in
your trunk, or you need to take the tire somewhere, you would do the next thing” may be viewed
as one t-unit. However, included in this one t-unit are three procedural steps. So, vital
information on the organization of responses to low-level cognitive-linguistic tasks would have
been lost if t-units were used for each load type. Thus, in order to preserve the organizational
structure of the procedural discourse, utterances were separated into procedural steps. As such,
no t-unit analysis was conducted on the procedural task.
The term t-unit is short for minimal terminal unit (Hunt, 1965). These units are useful for
fragmenting large complex utterances into smaller clauses for analysis while preserving the
original intent of the speaker. T-units can be used in a variety of ways to measure the complexity
and/or the efficiency of utterances produced during each level of cognitive-linguistic load.
Recent research on discourse analysis has found that pause and intonation criteria are more valid
in assessing spoken discourse than mean clause or sentence length. However, these criteria are
difficult to utilize consistently (Scott & Stokes, 1995), thus were not selected for analysis.
However, as a general rule, the greater the number of t-units, the more syntactically complex the
utterance. As such, total number of t-units was used to analyze the complexity of the responses
18
produced in the high-load tasks. T-units, defined as a complete clause with any associated
subordinate clauses, are more reliable for transcribing participant utterances for narrative tasks
(Gaies, 1980). This operational definition was important, since analysis of the transcripts in
SALT relies on the accurate division of utterances into t-units. The total number of t-units was
computed by SALT and used as a measure of complexity for discourse produced by the
participants.
Specificity of an utterance was measured in two ways. Word specificity, examined the
precision of word use in an utterance. Previous research has shown that neurologically intact
individuals will often use nonspecific words (NSW) to designate an object or an idea (EzratiVinacour & Levin, 2001) when asked to construct discourse in a pressured situation.
Consequently, nonspecific utterances were selected (nonspecific steps NSS, and NSW) as a
dependent measure to assess the clarity of participants’ narrative in low and high-load
conditions.
Fluency
As previously discussed, the performance on one or more tasks can deteriorate when
there is similarity between the modalities of the individual tasks. For example, the fluency of
speech and/or the rhythm of gait may be disrupted when a person is walking and talking at the
same time. This disruption might be due to the fact that both speaking and walking are motoric
tasks that rely on similar neural structures for accurate and precise execution (Frak, Cohen, &
Pourcher, 2004). Studies of persons who stutter under dual-task conditions have shown an
increase in dysfluencies when speech is overlaid on a secondary cognitive or motoric task
(Bosshardt, 1999). These results may suggest that the simultaneous completion of speech and a
secondary motor or cognitive task may cause increased dysfluency in speech production.
Moreover, the gradual increase in complexity of the verbal task in combination with the
secondary task may further exacerbate dysfluencies in an individual (Kleinow & Smith, 2006).
Since the current investigation incorporated an increase in cognitive-linguistic load
complexity across dual-task conditions, measures of word and utterance repetitions and revisions
as well as interjections (e.g. oh, um, or uh) were selected as dependent measures. These
measures were selected to examine how increasing cognitive-linguistic load might affect the
fluency of adults during execution of a common dual task – walking while talking. While a
frequency count of individual dysfluency types will be obtained, the measures will be collapsed
19
into one proportion of occurrence measure for analysis. Proportions of dysfluency were
calculated by dividing the total number of disfluencies by the total number of words uttered,
these proportions served as an index of speech fluency.
Gait Measures
As previously reviewed, gait can be measured a variety of ways using the GAITRite©
system. The variety of parameters measured by the GAITRite© software are divided into two
categories. The first category, temporal parameters, includes those variables that quantify the
timing features of an individual’s gait for each pass on the walkway. The temporal parameter
chosen for the current investigation was double support time. According to the GAITRite©
manual, double support, measured in milliseconds (ms), is “the time elapsed between First
Contact of the current footfall and the Last Contact of the previous footfall, added to the time
elapsed between the Last Contact of the current footfall and the First Contact of the next
footfall”. During a gait cycle, the double support time for all footfalls is totaled and divided by
the overall time spent on the gaitmat to obtain a percentage. In essence, the GAITRite© system
obtains a percentage of time spent stabilized on two feet for each pass on the walkway. During
execution of cognitive-linguistic tasks while walking, an increase in the percentage of time on
double support may be indicative of a compensatory need for increased stability while carrying
out both tasks simultaneously.
The second category of measurement on the GAITRite© system includes spatial parameters.
Measured in centimeters (cm), spatial variables consist of those parameters that assess distance
aspects of gait. A spatial variable that quantifies the distance between the heel contact of one foot
to the heel contact of the same foot on consecutive steps is stride length. To assess the
consistency of each participant’s stride during a full gait cycle, the current investigation
examined the differences in stride length across conditions (normal walk, low and high
cognitive-linguistic load) to examine if there was variability in gait. The literature on gait
suggests that increased stride variability may reflect an increased effort to stabilize the gait
pattern (Danion, Varraine, Bonnard, & Pailhous, 2003). However, previous research on stride-tostride variability does not provide a consistent model for calculating this measure. The
GAITRite© system averages stride length for each pass on the mat. Therefore, in the current
investigation, this measure was represented as the mean of those averaged stride lengths across
20
each condition. Decreased stride length as cognitive-load increases may point to an inability to
execute a more demanding discourse load while walking.
The last dependent measure selected to represent gait, which incorporates both temporal
and distance parameters, was the functional ambulation profile (FAP). The FAP score is a ratio
of the participants’ leg length and step length to the normalized step time. In examining gait
differences, the FAP score has been found to be a more predictive of fall risk as a measure of gait
performance than evaluating independent gait measures alone (Nelson, 2005). An FAP score of
90 – 100 is considered to be within the range of healthy adults (Nelson, Certo, Lembo, Lopez,
Manfredonia, Vanichpong, & Zwick, 1999). A score of 90 or below on a pass on the GAITRite©
system may indicate that that particular individual may be at risk for a fall.
Analyses
Descriptive Statistics
Descriptive statistics (means, standard deviations) were calculated for each dependent
measure to examine the distribution characteristics of group performance across baseline and the
low-load and high-load dual task conditions.
Inferential Statistics
Because the low-load and high-load cognitive-linguistic tasks had different dependent
measures (different organizational structures), separate t-tests were conducted for each level of
cognitive-linguistic load. To determine if there were significant differences in the number of
steps produced in the baseline condition and the number produced in the low-load condition, a
paired sample t-test was completed. Similarly, to examine differences in the number of t-units
produced in the baseline condition and the number produced in the high-load condition, a second
paired sample t-test was conducted. A one-way repeated measures ANOVA was conducted to
examine differences in the number of NSW and NSS produced across the baseline, low-load, and
high-load conditions.
A series of repeated measures analyses of variance (ANOVAs) were conducted to assess
for differences in fluency and gait measures across baseline, low-load, and high-load conditions.
A single factor repeated measures ANOVA was utilized to compare differences in the mean
proportions of dysfluency produced across the baseline low-load and high-load conditions.
Similarly, single factor repeated measures ANOVAs were adopted to analyze potential
differences across conditions with regard to gait parameters. Separate analyses were chosen for
21
a variety of reasons. First the FAP was considered separately because it is a composite of a
number of different parameters including double support time and stride length, thus, it was not a
mutually exclusive measure. Double support time and stride length were also run separately
rather than as a two factor repeated measures analysis of variance. The rationale for
implementing a two factor analysis is to investigate potential interaction effects. In the case of
these parameters, an interaction was expected based on previous research (Cicchitto, 2007;
Stierwalt, et al., 2006). Therefore, because the primary interest for this investigation was
examining the main effects of condition on these parameters, separate analyses were conducted
Reliability
Interjudge and intrajudge reliability was established on a randomly selected subset of 7
transcripts (approximately 20% of the sample). The primary investigator trained a second rater
using the linguistic complexity and fluency coding conventions previously outlined. Once
definitions were agreed upon, the two raters recoded 20% of the transcripts and calculated
percent agreement for each of the dependent measures with regard to speech analysis. Gait
parameters were measured objectively through the GAITRite© System, thus, not subjected to
reliability analysis. The primary investigator established intrajudge reliability using the same
recoded sample.
22
CHAPTER 3
RESULTS
The effects of manipulating cognitive/linguistic load during the dual task of walking
while talking are shared below, beginning with measures of linguistic complexity, followed by
the effects on an index of fluency, and finally on measures of gait. For each major research
question, descriptive statistics are presented first, followed by the results of inferential analyses.
Reliability
In the examination of reliability for coding the speaking tasks, interjudge reliability for
procedural transcripts was 99% in the division of procedural discourse into steps. Intrajudge
agreement for procedural transcripts was 98%. When the narrative discourse was considered,
both interjudge and intrajudge reliability for determining the number of t-units was 99%.
Evaluation of fluency measures between judges revealed that agreement was 71% while
intrajudge reliability for agreement in fluency ratings was 75%).
Linguistic Complexity
Descriptive Statistics
To examine distribution characteristics according to linguistic complexity means and
standard deviations were calculated for the dependent measures across baseline, low-load, and
high-load conditions. As a reminder, the baseline condition, was the condition where
participants performed tasks while seated in a chair. During the baseline condition participants,
as a group, produced a mean number of procedural steps of 26.80 steps (SD = 12.83). The mean
number of t-units produced was 14.47 t-units (SD = 11.48). With regard to non-specific
words/steps there were only 8 instances elicited in the baseline condition. Of interest to note was
that five of those eight were elicited from one participant. The remaining three instances were
distributed between two other participants. During the low load condition, the mean number of
procedural steps elicited was 9.66 steps with a standard deviation of 2.81. No instances of nonspecific words/steps occurred during this condition. The mean number of t-units was 5.47 t-units
with a standard deviation of 1.91 for the high load condition. Once again, there were no
instances of non-specific words/steps that occurred during this condition. This low incidence of
non-specific words/steps negated the possibility of statistical comparison; therefore, nonspecific
words/steps were not subjected to additional analyses.
23
Inferential Statistics
Because the dependent measures utilized in the low and high conditions were not
uniform (# of procedural steps vs. t-units), separate paired t-tests (two tailed) were conducted to
compare performance from these conditions to their parallel performance established during
baseline measures. The t-test conducted that compared the number of procedural steps produced
in the baseline condition to those produced during the low-load condition revealed a significant
difference (t (1,35) = 7.75; p < .001 (Figure 4)). The significant difference was related to the fewer
number of procedural steps that were produced during the low-load of producing procedural
discourse.
As with the low-load comparison, a paired t-test revealed a significant difference between
the number of t-units produced during the baseline condition and the number produced during
the high-load condition (Figure 5; t (1,35) = 4.82; p < .001).
Again, the difference reflected
significantly fewer t-units that were produced during the high-load condition of completing a
story.
Fluency Measures
Descriptive Statistics
To investigate the effects of walking while talking on the speech motor system, an index
of fluency was examined by calculating proportional means and standard deviations of
disfluencies (repetitions, revisions, interjections) across baseline, low load, and high load
conditions. In the baseline condition, the mean proportion of dysfluency during the low- load
task was .014 (SD = .015). The mean proportion of dysfluency during the high-load task was also
.010 (SD = .012). During the low-load condition of performing procedural discourse while
walking, the mean proportion of dysfluency was calculated as .005 with a standard deviation of
.009. Similarly, the mean proportion of dysfluency was .005 with a standard deviation of .008
during the high-load condition which required story completion.
Inferential Statistics
A repeated measures analysis of variance (RM-ANOVA) was employed to compare
mean proportions of disfluencies across the baseline, low, and high conditions (Figure 6). The
results of the RM-ANOVA revealed that the proportion of disfluencies produced in the baseline
to those produced during the low-load and high-load conditions revealed no significant
difference (F (2,34) = 1.27, p=.29, partial ŋ2=.07).
24
Gait Parameters
Descriptive Statistics
The distribution characteristics (means and standard deviations) for the gait parameters of
double support time, stride length, and FAP, were calculated across baseline, low-load, and
high-load conditions. As a reminder, the baseline condition for gait reflected data collected
during a walk across the GAITRite© system without an accompanying speaking task. To
differentiate this baseline condition from the baseline condition which incorporated speaking, it
will be noted as baselineG. In the baselineG condition, the mean percentage of double support
time was 24.82% (SD = 3.40%), and the mean stride length was 136.50cm (SD = 11.17cm) and
the mean FAP score was 97.06 (SD = 2.86). During the low-load condition, the mean percentage
of double support increased to 27.05% with a standard deviation of 3.95%. The mean stride
length and FAP score during the low-load condition were 125.55cm (SD = 13.65cm) and 93.83
(SD = 6.10), respectively. When gait performance was measured during high-load, the mean
percentage of double support was 26.57% with a standard deviation of 4.14%. Performance with
regard to stride length during high-load demonstrated a mean stride length of 126.19cm with a
standard deviation of 13.06cm. Finally, the mean FAP score was 94.63 (SD = 5.72).
Inferential Statistics
Repeated measures ANOVAs were also utilized to examine potential changes across
conditions for measures of gait. The RM-ANOVA conducted which compared the percentage
of double support across conditions revealed a significant difference (F (2, 34) = 39.02, p < .001,
partial ŋ2 = .69 (Figure 7)). Follow-up pairwise comparisons (utilizing a Bonferroni correction
for multiple comparisons) revealed significant differences between the percent of double support
produced during the baselineG condition and that obtained during both the low-load (p < .001)
and high-load conditions (p < .001). The difference between the low-load and high-load
condition for percentage of double support was not statistically significant however (p = .31). A
RM-ANOVA which compared stride length across baselineG, low-load, and high-load
conditions showed a significant difference (F (2, 34) = 74.35, p < .001, partial ŋ2 = .81 (Figure 8)).
Follow-up pairwise comparisons (utilizing a Bonferroni adjustment) revealed significant
difference between gait baseline and low-load (p < .001) and gait baseline and high-load (p <
.001) but not between low-load and high-load conditions (p > .99). Finally, the results the RMANOVA, which compared FAP scores obtained across baselineG, low-load, and high-load
25
conditions, was also significant (F (2, 34) = 6.81, p = .003, partial ŋ2 = .28 (Figure 9)). Again,
follow-up pairwise comparisons (utilizing a Bonferroni adjustment) were employed to determine
the location of differences between individual conditions. The results of these pairwise
comparisons revealed that the differences in FAP scores from baselineG to low-load and
baselineG to high-load conditions were significant, p = .002 and p = .025, respectively. As with
the other gait parameters, however, differences in FAP scores between low-load and high-load
conditions were not significant (p = .27).
26
CHAPTER 4
DISCUSSION
The general purpose of the current study was to examine the effects of cognitivelinguistic load on the discourse and gait performance of healthy elderly adults. Specifically, this
investigation sought to analyze the changes in measures of linguistic complexity, fluency, and
gait as the complexity of cognitive-linguistic load increased. Results indicated that the addition
of a secondary task effected measures of linguistic complexity, stride length, double base of
support, and FAP.
Influences of Cognitive-Linguistic Load on Linguistic Complexity Measures
Theoretically the increase in cognitive-linguistic load was thought to increase the
occurrence of non-specificity within the participants’ responses (Huang, 2000). Based on this
assumption, additional analyses were planned for the number of non-specific words (NSW) and
non-specific steps (NSS) generated in each condition, however, the frequency of occurrence of
these variables was so low that analyses were not possible. Inclusion of the fluency variables
were based on pilot data, which revealed that eight of ten participants produced NSW or NSS
across all conditions. However, occurrence of NSW and NSS did not occur with the participants
of this study. Eight total instances of NSW and NSS occurred with one participant producing five
of these eight instances. As such, number of NSW and NSS were not included in analyses of
linguistic complexity. Based on the results of this investigation it would appear that the low rate
of occurrence could indicate that the production of NSW’s or NSS’s is a highly individualized
event. That is borne out by the data, which reflect that most instances occurred in the speech of
one participant. So our apriori assumptions that nonspecificity might be an index of linguistic
disturbance when individuals were completing a dual task were not supported.
The number of procedural steps and number of t-units produced by healthy older adults in
this study decreased significantly between baseline, and low-load and high-load conditions.
Snow and colleagues (1998) categorize procedural steps into three hierarchically arranged
categories – essential, optional, and target steps. Essential steps are necessary for the accurate
completion of the goal whereas optional steps are helpful, but not required for task execution
(Snow, Douglas, & Ponsford, 1998). The target step is the attainment of the intended goal. In the
current study, participants included more optional steps at baseline than during low-load or high-
27
load conditions. A similar pattern was observed for t-units produced in the high cognitivelinguistic load level. Participants produced fewer t-units during baseline than during low-load
and high-load conditions.
In examining the number of steps and number of t-units, it was found that the
introduction of a secondary task was costly for linguistic complexity in both the low-load and
high-load conditions. The empirical evidence that the introduction of a secondary task may cause
the deterioration of one or both tasks is a well-researched phenomenon (Kahneman, 1973;
Wickens, 1991; Pashler & Johnston, 1998; Law, Logie, Pearson, Cantagallo, Moretti, &
Dimarco, 2004). In keeping with this theoretical line, it follows that the introduction of a
cognitive-linguistic task while the participants were walking would cause a decrease in linguistic
complexity. There are several plausible explanations for this pattern of performance.
First, changes in total number of steps or t-units from baseline to low-load and high-load
tasks may be attributed to the fact that participants were forced to allocate attention related
cognitive resources between the characteristics of walking and speech (Kahneman, 1973). That
is, participants not only had to concentrate on adequately responding to the discourse prompt, but
participants also had to focus on their gait. A division of resources may be the cause of an overall
decrease in the number of steps and t-units produced. A similar conclusion may be drawn for the
total number of procedural steps produced from baseline to low-load and high-load conditions.
This decrease in performance not only affected these measures of discourse, but as will be seen,
measures of gait as well. Another factor that may have influenced the total number of procedural
steps or t-units generated in the dual-task conditions was the presence of a time constraint.
When individuals walked the length of the walkway including the GAITRite© system, a
time limitation was naturally imposed on discourse production. Time pressure existed in the
dual-task condition in that participants only had the 44-foot length of the walkway to complete
verbal tasks as they walked. Unlike the experimental tasks which had this natural termination
point, the baseline tasks were unconstrained. That decision was based on data from a pilot study
which showed that when a time restraints were placed on the baseline responses, a loss of data
pertaining to linguistic complexity occurred. As such, participants were allowed as much time as
needed to respond to cognitive-linguistic stimuli during baseline. The lack of a time constraint
likely inflated the effect of cognitive-linguistic load from baseline to low-load and high-load
conditions in that participants inherently produced fewer procedural steps or T-units while
28
walking than while seated (see Table 8). Future research should include a time limit during the
baseline condition to account for what appear to be exaggerated dual-task effects, and thus,
would allow for stronger comparisons of cognitive-linguistic performance.
Of interest to note however, are investigations of linguistic complexity under time
pressure found that on similar tasks, individuals could speak at a fast rate in a short amount of
time and convey ideas as clearly or better than an individual who spends more time on task
execution. Thus, a person can be more productive in the face of time restraints than when
allowed to take as much time as needed to adequately complete the task (Scott & Windsor,
2000). Based on this finding, it follows that participants in the current investigation may have
been more productive during the low-load and high-load conditions under time restrictions than
during the baseline condition, a finding that was not observed here, however. The literature on
time constraints would suggest that the imposition of time limits may force an individual to more
strictly organize their thoughts so that they can complete the task in the time allotted. The
temporal limitations force the speaker to naturally provide additional detail by generating
utterances with more syntactic complexity in order to ensure that the listener comprehends the
intended meaning. This increased complexity of language may also cause an increase in the
proportion of disfluecncies (Masterson & Kamhi, 1991). That theory led to our desire to
measure speech fluency under varying cognitive load.
Influences of Cognitive-Linguistic Load on Fluency Measures
Unlike the language system, the oral motor system was not as heavily affected by the
increase in cognitive-linguistic task complexity or the addition of a second task. Studies of
fluency patterns in conditions of increasing cognitive-linguistic demands have found that
increasing linguistic demands, like the tasks presented in the current study, may cause
perturbations in the speech motor system (Kleinow & Smith, 2000; Scott-Trautman, Healey, &
Norris, 2001). One manifestation of that perturbation would have been demonstrated by an
increase in the number of disfluencies, yet the opposite was true in this case. Although
participants produced disfluencies infrequently, there was a decrease, rather than increase, in the
proportion of disfluencies produced in baseline and the proportion of disfluencies produced in
the low-load and high-load conditions. One possible explanation is that, like measures of
linguistic complexity, speech fluency is affected by time constraints.
29
Although it seems counterintuitive based on several research studies (Kleinow & Smith,
2006; Bosshardt, 1999) the presence of a time limit may have influenced participant performance
from baseline to low-load and high-load conditions by increasing fluency. Previous research on
the effects of time pressure on fluency showed that disfluencies decreased as the time pressure
increased (Lugo-Neris, 2005). Moreover, for adults with typical fluency, as was the case in this
study, stability in fluency is not unexpected because there is no communication disorder present
that might be exacerbated by the addition of a second task. Since the adults in the current study
were all observed to have typical fluency, the finding that the proportion of disfluencies
decreased while performing under the time pressured imposed by low-load and high-load task
conditions verifies these earlier findings. It is important to note however, that although overall
dysfluency decreased the difference across conditions was not a large one (not statistically
significant), thus, may not be enough to be a “real” difference at all. In addition to this temporal
explanation for decreased proportion of disfluencies across conditions, the present study may
also support theoretical explanations.
In addition to the time constraint explanation, there is another theory that might support
these findings. Attention allocation theory would suggest that attention related resources were
divided between the motoric act of speech and the motoric act of walking. The literature on
multitasking has found that when two tasks are similar in modality, the performance of one or
both tasks will suffer due to a reduction in shared resources (Kahneman, 1973; Law, Logie,
Pearson, Cantagallo, Moretti, & Dimarco, 2004). Given this familiar finding, intuition predicts
that fluency will decrease in the presence of the dual-task of walking while talking. However, in
the present study, the execution of two motor tasks had the effect of increasing fluency of speech
in participants. This result is in agreement with previous studies on the frequency of stuttering in
dual-task conditions (Bosshardt, 1999). Although the exact cause of increased fluency has not
been deciphered, investigators have proposed that in the presence of a secondary task,
individuals develop an increased awareness of their speech. This increased awareness of speech
produced increased fluency in speech. This increased awareness may be the cause of decreased
proportion of disfluencies for participants under the low-load and high-load conditions for the
current investigation. Furthermore, the increased attention to speech may have resulted in
increased fluency because additional resources were recruited for executing the movements of
speech. However, as observed in changes in gait measures, the recruitment of additional
30
resources for speech may have left fewer resources for walking thus causing a decrease in gait
performance.
Second, as previously mentioned, adults with typical fluency are thought to have a more
stable oral motor system than adults who stutter. As such, the oral motor system of adults who
stutter may be more predisposed to produce disfluencies under conditions that tax their capacity
for fluent speech. This assumption is in agreement with the demands and capacities model of
stuttering. Similar to the theory of allocated resources, this model states that stuttering occurs
when there is an interaction between the innate capacity of the individual and the demands of the
environment including the requirements of the speaking task (Starkweather & Gottwald, 2000;
Siegel, 2000). Following the logic of this theory, rationale for the decrease in proportion of
disfluencies in participants may be found in the fact that all participants were typically fluent.
Hence, the demands of walking while executing low-load and high-load tasks did not cause a
breakdown in their oral motor system since their capacity for fluent speech was not taxed beyond
their ability. However, further research is needed in order to determine if the same pattern of
increased fluency across cognitive-linguistic load between baseline and dual-task conditions
exists for persons with an impairment in the oral motor system.
Influences of Cognitive-Linguistic Load on Gait Measures
Of the many variables that can be explored to assess alterations in gait, differences in
stride length has been found to be the most reliable in determining gait control (Danion,
Varraine, Bonnard, & Pailhous, 2003). An increase in stride length may indicate a loss of gait
stability. In the presence of a secondary task, such as talking, this increase may be a sign of an
inability to execute both cognitive tasks simultaneously. In the current investigations, stride
length in the silent walking condition was significantly greater than the length observed in the
low-load and high-load conditions. These results are not in agreement with research of dual-task
paradigms involving verbal tasks and walking (Dubost, Kressig, Gonthier, Herrmann, Aminian,
Najafi, & Beauchet, 2006). The differences in results may lie in dissimilarities in the
methodology of previous studies and the current investigation.
The verbal tasks included in previous studies were comprised of well-practiced motor
tasks like counting, serial subtraction, and other mental calculations. Previous research utilizing
verbal tasks as the cognitive-linguistic load presented during gait found that stride length
decreased as complexity of task increased. The current investigation also found that stride length
31
decreased as complexity of the cognitive-linguistic task increased, though these findings were
not as apparent as those found in past studies. It may be that verbal tasks that are conversational
in nature cause more of an interaction between cognitive resources used for gait and for
discourse. This increased interaction in cognitive resources might cause individuals to lengthen
their stride in an effort to stabilize their gait (Danion, Varraine, Bonnard, & Pailhous, 2003). Yet,
this assumption requires further investigation to determine if an interaction exists and, if so, how
much the interaction influences gait parameters. Moreover, given the interrelated nature of gait
variables, the presence of this interaction in the spatial parameters could carry over to temporal
parameters such as double base of support.
Compared to standing, walking may be considered a less “stable” activity. Double base
of support while walking is a stabilizing factor during a normal gait cycle (Winter, Patla, Frank,
& Walt, 1990). In the current investigation, the addition of a cognitive-linguistic task during gait
caused an increase in the percentage of double base of support. This finding is consistent with
previous research on gait in healthy elderly adults that also found an increase in percentage of
double base of support as the complexity of cognitive-linguistic load increased (Stierwalt, Toole,
LaPointe, Maitland, & Apel, 2006). Although past investigations utilized rote tasks, this robust
result of increased double support time could indicate that the inclusion of a cognitive-linguistic
task involving discourse might call for a similar need to increase the stability of gait. Healthy
elderly adults may increase the time in double base of support in an effort to stabilize their gait in
a compensatory effort to avoid a fall. This need for stabilization was also seen with the FAP
score.
The FAP score was developed based on the premise that aging effects stride length and
velocity more than any other gait parameter (Nelson, Certo, Lembo, Lopez, Manfredonia,
Vanichpong, & Zwick, 1999). This ratio of velocity to stride length has been accepted as a valid
predictor of fall risk in the study of gait in healthy as well as clinical populations. In the present
study, a significant decrease in FAP score occurred from baseline to low-load and high-load
conditions. Not only did FAP scores drop from baseline to low-load and high-load conditions,
but overall, FAP scores also fell below 95. As previously discussed, a score below 95 indicates
the potential for a fall. These findings are in agreement with previous research on the effects of
cognitive-linguistic load on gait in healthy aging adults that also found a significant decrease in
FAP scores when cognitive-linguistic load was included (Cicchitto, 2007). Moreover, an effect
32
of cognitive-linguistic load can be assumed from these results while inferentially, it may be
implied that the presence of a cognitive-linguistic task increased the potential for fall risk in
these participants.
Although changes in all gait parameters was observed across baseline, low-load, and
high-load conditions, it should be noted that the greatest difference in gait performance was
observed from baselineG to low-load and baselineG to high-load conditions. Theoretically, the
low cognitive-linguistic task is more complex than the high cognitive-linguistic task (Wikberg,
1992) Yet, the gait performance for these two conditions resulted in almost even means for each
level of linguistic complexity. It appears that the measures of discourse complexity selected for
this investigation were equally detrimental to gait. Perhaps that will be true of other measures of
discourse. Additional study is certainly warranted to examine this phenomenon. Interestingly,
variations in gait performance resulted in statistically significant results across measures of
discourse, whereas fluency measures were seemingly unaffected. While more fluent speech
while walking may indicate an increase in communicative efficiency, the cost of that increased
efficiency may have been an increased risk for a fall. This assumption may be especially true for
adults who are neurologically compromised.
Clinical Implications
Changes in gait and/or cognitive-linguistic measures may result from the attempt to
complete two cognitive tasks at the same time. In the current investigation, participants were
asked to respond to cognitive-linguistic stimuli while simultaneously executing the cognitive
task of walking. The view that walking involves cognitive processing is a relatively novel
concept to the study of movement. However, kinesthetic researchers have proposed that the
coordination of limb movements into a smooth, rhythmic gait requires attention related
resources, thus making walking less of an automatic task than previously thought (Hausdorff,
Yogev, Springer, Simon, & Giladi, 2005; Yogev, Giladi, Peretz, Springer, Simon, & Hausdorff,
2005). Many investigations of dual task paradigms using verbal production simultaneous with
walking have found significant changes in gait in individuals with and without cognitive
impairments (Dubost, Kressig, Gonthier, Herrmann, Aminian, Najafi, & Beauchet, 2006; Yang,
Chen, Lee, Cheng, & Wang, 2007; Toulotte, Thevenon, Watelain, & Fabre, 2006; Hollman,
Salamon, & Preist, 2004).
33
Springer and colleagues (2006) investigated the effects of dual tasking on gait variability
in young adults and older adults who were classified as fallers or non-fallers based on self-report.
Subjects were asked to walk while listening to a passage via headphones, while listening and
monitoring for the occurrence of specific phonemes, and walk while counting backward by
sevens. Velocity and swing time were assessed for changes in gait parameters. The results of this
study showed that young adults and older adults classified as non-fallers exhibited similar
walking patterns under dual task conditions. This pattern was comprised of decreasing velocity
for young adults and decreasing both velocity and swing time for non-fallers. When executing
any three of the cognitive-linguistic tasks, decreasing the speed and balance parameters had the
effect of stabilizing the gait of these two subject groups. Similar to the current study, these
results highlight the potentially devastating effect of distraction on gait in healthy aging adults.
Due to the decreased neural capacity for execution of cognitive tasks that might be a result of
neurologic injury, neurologically compromised adults may be more susceptible to changes in
gait.
Given that in the rehabilitation of clinical populations, individuals with brain injury are
often required to multitask, assessment of the effects of multiple task execution on measures of
task performance is warranted. Past research has focused on the neuromuscular aspects of gait to
decrease the amount of falls in this population (Wolfson, 2001). However, due to the overlapping
nature of neural substrates for movement and language recent research has begun to focus on the
cognitive-linguistic aspects of walking while talking (O’Shea, Morris, & Iansek, 2002). Results
of investigations that have examined the effects of cognitive-linguistic load in individuals with
neurodegenerative disease and acute neurological damage have shown differences in gait as well
as changes in linguistic performance in dual task paradigms (Haggard, Cockburn, Cock,
Fordham, & Wade, 2000; Regnaux, David, Daniel, Ben Smail, Combeaud, & Bussel, 2005;
Yogev, Giladi, Peretz, Springer, Simon, & Hausdorff, 2005; Stierwalt, LaPointe, Toole,
Maitland, & Wilson, 2006). According to the capacity sharing model previously described
(Kahneman, 1973), these results may point to an inability to efficiently utilize limited capacity
for task completion and/or divide attention related resources in the presence of simultaneous
execution of walking while performing cognitive-linguistic tasks. However, further research is
needed to determine if the tasks are depleting the reduced resources of individuals with
34
neurological compromise or if the performance of these individuals is a consequence of dividing
attention.
Comparing performance of neurologically intact individuals with that of individuals who
are neurologically compromised is an important step in the development of treatment protocols
aimed at cognitive, linguistic, or motoric rehabilitation. Defining the cognitive-linguistic
hierarchy of tasks that may influence balance, gait, and general safety issues may lead to a
functional, sensitive assessment of cognitive-linguistic function in adults with neurological
compromise (Yogev, Giladi, Peretz, Springer, Simon, & Hausdorff, 2005). Utilizing a more
sensitive assessment tool that might more accurately reflect levels of potential risk or motor
breakdown may lead to more tailored approach to the remediation of cognitive and motoric
ability. In addition, this tailored treatment could also carry over to improved outcomes for
neurologically compromised individuals.
Future Directions
This study is groundwork for future investigations evaluating measures cognitivelinguistic performance during the simultaneous execution of gait. It may be of interest to assess
the effects of more varied types of cognitive-linguistic tasks to better understand the possible
interaction in the cognitive process involved in linguistic and motoric tasks. In addition, utilizing
more specific measures of cognitive organization, such as story grammar components, may
improve understanding of the cognitive processes underlying linguistic performance. Also, the
usual arrangement of walking while talking is for individuals to walk side-by-side. The inclusion
of a second person to walk slightly behind participants, so as not influence the pace of participant
gait, may increase the ecological validity of the protocol, thus providing more insight into the
effects of cognitive-linguistic load on gait parameters in the daily lives of healthy aging
individuals. Moreover, if individuals in clinical populations are more susceptible to the
devastating effects cognitive-linguistic load has on gait, then future studies in this area should
include examination of dual-task performance in neurologically compromised adults using
measures of discourse.
Summary
The specific aim of the current investigation was to examine the effects measures of
linguistic complexity and fluency with measures of gait in healthy aging adults. In general, this
study highlighted the need to further evaluate the potential interaction of cognitive-linguistic and
35
motoric parameters in a common dual-task, walking while talking. The results of this study
revealed that as cognitive-linguistic load increased, linguistic complexity and fluency decreased.
In addition, stride length decreased, percentage of double support increased, and functional
ambulation profiles (FAP) decreased in the presence of a secondary cognitive-linguistic task.
Clinical implications of walking while talking include cognitive-linguistic impairment and
possible adjustment of patients’ plan of care. Future research should focus on extending the
ecological validity of cognitive-linguistic tasks in healthy aging adults as well as include adults
with neurological compromise to assess and to understand the cognitive-linguistic and motoric
aspects of clinical populations.
Investigations of the effects of cognitive-linguistic load on gait will continue to explore
the involvement of task complexity in cognitive and motoric performance using a variety of
cognitive-linguistic task loads. Research that utilizes more ecologically valid tasks such as
discourse rather than basic calculations or finger tapping are relatively new to the study of dualtask effects in neurologically intact and neurologically compromised adults. An increase in falls
in clinical populations and in neurologically intact older adults has prompted an investigation of
the neuromuscular as well as cognitive-linguistic effects on gait (O’Shea, Morris, & Iansek,
2002; Stierwalt, LaPointe, Toole, Maitland, & Wilson, 2006; LaPointe, Stierwalt, Maitland,
Heald, Wilson, Cicchitto, & Apel, 2007). Given that many activities depend on the same neural
substrates for accurate execution as well as the presence of a breakdown of skeletal musculature
with advancing age, it follows that age-related gait changes combined with paralleled changes in
cognition, the effects can have detrimental effects for the individual. Understanding the factors
that cause a fall risk may lead to the development of a dual-task treatment protocol that may
reduce the risk in healthy aging adults as well as adults with neurological compromise.
36
Figure 1. Model of Resource Allocation in Attention (Kahneman, 1973)
37
Table 1
Eight Criteria for Classification as Multitasking adapted from Burgess (2000)
Criteria for Multitasking
1. Presence of Multiple Tasks
Explanation of Criteria
1. Managing multiple household chores
in preparation for visitors
2. Eight hours to complete chores before
arrival of visitors
3. Set up guest room, dust the furniture,
prepare hors d’ouvres
4. Colleague calls while chopping
vegetables for hors d’ouvres
5. Cannot set up guest room until
laundry is washed and dried
6. Tools and planning involved for guest
room set up, dusting, and food
preparation are different
7. Adequate guest room set up and
cleanliness of furnishings as well as
completeness of food is established
according to hostess’s standards not
the guests
8. During execution, feedback on
progress is not present; performance
is updated according to selfestablished goals.
2. Time Constraints
3. Execution of Tasks One at a Time
4. Unexpected Disruptions
5. Delay of Intention
6. Dissimilarity of Task Features
7. Self-established Goals for Execution
8. Absence of Immediate Feedback
38
Table 2
Discourse Tasks for Low and High Cognitive-linguistic Load
Mid: Describe how to perform each of these tasks.
Tell me how to change a tire.
Tell me how to make a sandwich.
Tell me how to withdraw money from a bank account.
Tell me how to write a letter.
High: I am going to start a story. When I stop speaking, please complete the story.
The audience fell silent. They had cheered the Warriors through eight innings against
their fiercest rivals. Only one run was required to defeat the Pythons, who were undefeated this
season. A winning game would clinch their title as champions and earn them the precious trophy.
As Pedro took his position at the plate, he sensed tension in the stands. He knew the fans
regarded him as a beginner and were perhaps remembering his errors in previous games. Pedro
knew he had talent, but could he perform well in a pinch? Then he glimpsed the coach’s steady
gaze upon him and felt a quick surge of confidence.
Two boys rode the bus downtown to shop for Christmas presents for their families. They
decided to begin their day by window-shopping so that they could get an idea of what the stores
had to offer. One brightly decorated store grabbed the boys’ attention so they decided to go
inside. They browsed the aisles of assorted items. Excited about the huge sale in the entire store,
the boys filled their cart with gifts. While looking at books about animals, one boy saw a book
about horses. He said, “My sister would like this book, but I don’t have much money left.”
The biologist scanned the mouth of the cave looking for fruit eating bats. On this
particular outing he hoped to snare a few bats for his study of the fruit bat’s role in the spread of
seeds. Within the cave was a pool in which numbers of bats had deposited pieces of fruit, along
with their droppings. The result was a smelly brew with the consistency of a thin stew. The
biologist braced himself and hiked through the pool while struggling with his gear and holding
back waves of nausea. When he was up to his waist, the beam from his flashlight panicked the
sleeping bats, which flying wildly overhead caused him to drop his flashlight.
A lot of what we know about sharks comes from the work of Eugenie Clark. Eugenie is a
scientist who studies the behavior of sharks. While diving in the ocean she has observed sharks
in their natural environment. In her work at a marine laboratory, she also designed a project for
studying sharks that have been captured. Her work has proven that sharks are smart and have a
good memory. Her research continually brings her face to face with sharks. In spite of what
many people think she believes that sharks are not dangerous so she continues to take risks.
39
Table 3
Situational Discourse Semantic (SDS) Model Adapted from Norris & Hoffman (1993)
SITUATIONAL
CONTEXT
DECONTEXTUALIIZED
Level 10: LOGICAL
Mental objects created
through language
alone—can’t see it (e.g.
linguistic
processing)
Level 9: SYMBOLIC
Make up a story/give
information that could be
concrete, but without
Context
Level 8: RELATIONAL
DISCOURSE
CONTEXT
Level 10: INTERACTIVE
SEMANTIC
CONTEXT
(Multiple topics)
Different experiences happen to different characters but in the end it all comes
together, or the characters are united by a common story (e.g., Seinfeld)
---------Level 9: COMPLEX (Parallel
perspectives)
Story is told by several characters, but they all share their perspectives of the
same events (e.g., we all had same experience but processed it differently)
---------Level 8: COMPOUND (Two
Level 6:
EGO-CENTERED
Talking about your own
experience—retelling
about your vacation
DECONTEXTUALIIZED
CONTEXTUALIIZED
Story contains at least two complete episodes, told from the perspective of a
single character.
Level 5: LOGICAL
Make up a story, support
with
pictures or print
Level 4: SYMBOLIC
Pretend play with
miniatures
or retell a story with
pictures/
print/toys
Level 3: RELATIONAL
Play or talk about a routine
using real objects
Level 2: DECENTERED
Talk about a sensorimotor
event as it is happening
Level 1:
---------Story contains an initiating event, which sets up problem to be solved/goal to
be achieved. Characters then formulate plans/make attempts (can be mental or
physical attempts) to solve the problem/achieve the goal. The consequence of
these plans/attempts is communicated.
CONTEXTUALIZED
Figurative language, indirect
meaning
Level 8: EVALUATION
Judgment/value, significance.
Key words: weighted
comparisons (better than)
-----Level 7: INFERENCE
Forming meaning beyond
what
is directly stated. Key words:
conjunctions.
-----INTERPRETATION
Psychologically assigning
meaning based on perceptual
cues. Key words: “I think …”
-----Level 5: ATTRIBUTION
--------SEQUENCE
Ideas are expressed in a cause-effect chain order(key words: conjunctions) that
makes sense, but there is no overriding plan or intent.
----------
Characteristic qualities,
information about state.
-----Level 4: DESCRIPTION
Suggests relationships between
objects, events, and agents.
How actions relate.
Ideas are linked in ARBITRARY temporal order (key words: temporal markers)
but no clear cause-effect relationship.
-----Level 4: ORDERED
SEQUENCE
Level 3: LABELING
---------Level 3: DESCRIPTIVE
LIST
Ideas are organized by topic only—a grocery list of information. There are
categorical links, but no temporal or causal links.
---------Level 2: COLLECTION
Utterances that may be loosely associated but are not structured by category,
time, or cause-effect. Topic is easily changed because it is only loosely
established.
-----------
EGO-CENTERED
Sensorimotor exploration
of objects on own body
-----Level 9: ANALOGIES
Level 6:
Level 6: ABBREVIATED (Initiating event, Plan/Attempt, Consequence)
Level 5: REACTIVE
E.g., Why words are words,
“syllableness”
-------
Or more episodes)
---------How actions/characters/
Level 7: COMPLETE (Initiating event, Plan/Attempt, Consequence,
etc.
Resolution)
within an event relate: e.g.
Story contains an initiating event, which sets up some problem to be solved/
talking about events at a
goal to be achieved. The characters then formulate plans or make attempts (can
birthday party
be mental or physical attempts) to solve the problem/achieve the goal. The
Level 7: DECENTERED
Talking about an observed consequence of these plans/attempts is communicated, and then a resolution is
included –the moral of the story.
experience - didn’t actually
put own body through it;
retelling a tv show/movie
Level 10:
METALANGUAGE
Level 1: DISCRETE
EVENTS/ISOLATED FACTS
Disconnected information; utterances that are basically reactions to the
moment.
40
Naming wholes, or listing
parts
of wholes, directly matches
perception.
-------Level 2: INDICATION
Nonlinguistic (e.g. pointing).
Meaning is only understood in
context.
-----Level 1: REACTION
No communicative intent or
purpose
Table 4
Means and Standard Deviations of Participants’ Age, MoCA scores, Days of Physical Activity,
and Minutes of Physical Activity
Mean
Standard Deviation
Age
65.02
7.83
MoCA Score
27.13
2.07
Days of Activity
3.27
1.18
~30 – 60
~10 – 20
(days per week)
Minutes of Activity
(minutes/day of activity)
41
Figure 2. Mean Minutes of Physical Activity for Participants who Reported Routine Engagement
in Physical Activity
42
10
10
8
8
Figure 3. Education Level for Participants
43
Figure 4. Internal Review Board Letter of Approval
44
Figure 5. Informed Consent Form
45
Figure 5. – continued.
46
Table 5
Means and Standard Deviations of Total Number of Procedural Steps and Total Number of Tunits for Low and High Cognitive-Linguistic Load Levels Across Baseline and Dual Task
Conditions
Cognitive-Linguistic Load
Baseline
Dual Task
Low-Load: Procedural Steps
Mean
26.80
SD
12.83
Mean
9.66
SD
2.81
High–Load: t-units
14.47
11.48
5.47
1.91
47
26.80 (12.83)
9.66 (2.81)
Figure 6. Mean Number of Procedural Steps across Baseline and Low-load Conditions
48
14.47 (11.48)
5.47 (1.91)
Figure 7. Mean Number of T-units across Baseline and High-load conditions
49
Table 6
Means and Standard Deviations for Proportion of Disfluencies across Baseline and Dual-task
Conditions
Cognitive-Linguistic Load
Baseline
Dual Task
Low-Load
Mean
0.014
SD
0.015
Mean
0.005
SD
0.009
High-Load
0.010
0.012
0.005
0.008
50
0.014 (0.015)
0.010 (0.012)
0.005 (0.009)
0.005 (0.008)
Low-level cognitive-linguistic load
High-level cognitive-linguistic load
Figure 8. Mean Proportion of Dysfluency across Baseline, Low-load, and High-load Conditions
51
Table 7
Means and Standard Deviations of Double Support Base (DSB), Stride Length (SL), and
Functional Ambulation Profile (FAP) Across BaselineG, Low-load, and High-load Conditions
Cognitive-Linguistic Load
DSB
SL
FAP
G
Baseline
Mean
24.82
SD
3.40
Mean
136.49
SD Mean
11.17 97.06
SD
2.86
Low-load
27.05
3.95 125.55
13.65 93.83
6.10
High-load
26.57
4.14
13.06 94.63
5.72
52
126.18
27.05 (3.95)
26.57 (4.14)
24.82 (3.40)
Low-level cognitive-linguistic load
High-level cognitive-linguistic load
Figure 9. Double Support Base for BaselineG, Low-load, and High-load Conditions
53
136.49 (11.17)
125.55 (13.65)
126.08 (13.06)
Low-level cognitive-linguistic load
High-level cognitive-linguistic load
Figure 10. Stride Length Differences for BaselineG, Low-load, and High-load Conditions
54
97.06 (2.86)
93.83 (6.10)
94.63 (5.72)
Low-level cognitive-linguistic load
High-level cognitive-linguistic load
Figure 11. Functional Ambulation Profile (FAP) for BaselineG, Low-load, and High-load
Conditions
55
Table 8
Example of Participant Responses across Baseline, Low-load, and High-load Conditions.
Baseline Condition: Low cognitive-linguistic load
C Okay.
C Well first of all you need to make sure that your car is in park {C laughs} so it doesn’t roll.
C And then you would take the jack and put it underneath the area where you would, the tire needs to be
changed.
C And I think, I don’t know what that bar is called but it goes underneath the bar there.
C And then you jack it up so the tire is above the ground a few inches so you’ll be able to remove it once
you get the lug nuts off.
C So you would take your tire iron and go and loosen your lug nuts.
C Take them off.
C And then you would pull your tire off.
C And then depending on whether you’ve got a spare tire in your trunk.
C Or you need to take the tire somewhere.
C You would do the next thing.
C If you have a spare tire in your trunk you would take it out.
C Put it on there.
C Put the lug nuts back on it.
C Lower the car back down.
C And then you’ve completed changing the tire.
C If you’ve got to take your flat somewhere to be fixed you would hopefully have a cell phone.
C And call someone to come get you.
C Take you to the tire place.
C Get it repaired.
C And bring you the repaired tire back or a new tire.
C Put it on.
C Put the lug nuts back on.
C And then lower it down.
Baseline Condition: High cognitive-linguistic load
C So the boy talked to his brother and they put a plan together.
C They decided they’d go up to the counter and talk to the person at the checkout counter.
C And there they asked that person could they possibly do something to work out that they could
purchase that book also.
C And the person behind the counter said, “Well actually you could. Here’s a little feather duster and see
that shelf over there? You can dust that shelf off and once that’s done you can include that book with your
purchase.”
Low-load Condition
C I will, gunna go to my bank Zurich Switzerland.
C And I’m going to take a withdrawal slip.
C And fill out my account information.
C And then I am going to put the amount in.
C And take it to the teller.
C And she is going to give me my money.
High-load Condition
C As the gentleman looked for his flashlight he gently knelt down.
C Trying to keep his face from that ugly bat’s stew and felt around in the dark.
C And he was able to retrieve it and the batteries kept on going.
56
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64
BIOGRAPHICAL SKETCH
Kimberly R. Wilson obtained her Bachelor’s degree form Winthrop University and her
Master’s degree from the University of South Alabama. Her current research focuses on
cognitive-linguistic interactions in neurologically compromised populations, dysphagia, and
motor speech disorders. Kimberly’s research has been presented at state, national, and
international conferences.
65