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Theses and Dissertations
2013
Effect of secondary motor and cognitive tasks on
timed up and go test in older adults
Anuradha Mukherjee
The University of Toledo
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A Dissertation
entitled
Effect of Secondary Motor and Cognitive Tasks on Timed Up and Go test in Older
Adults
by
Anuradha Mukherjee
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Doctor of Philosophy Degree in Exercise Science
_________________________________________
Dr. Charles Armstrong, Committee Chair
_________________________________________
Dr. Phillip Gribble, Committee Member
_________________________________________
Dr. Martin Rice, Committee Member
_________________________________________
Dr. Peggy Arnos, Committee Member
_________________________________________
Dr. Patricia R. Komuniecki, Dean
College of Graduate Studies
The University of Toledo
December 2013
Copyright 2013, Anuradha Mukherjee
This document is copyrighted material. Under copyright law, no parts of this document
may be reproduced without the expressed permission of the author.
An Abstract of
Effect of Secondary Motor and Cognitive Tasks on Timed Up and Go Test in Older
Adults
by
Anuradha Mukherjee
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Doctor of Philosophy Degree in Exercise Science
The University of Toledo
December 2013
Background: Changes in gait due to simultaneous performance of an attentiondemanding dual task, such as walking while talking on the phone, may be caused by
competing demands for attentional resources. As dual-tasking during gait is common,
assessment of this paradigm is becoming highly important for mobility research. The
objective of this study is to examine changes in gait and verbal response while
performing a timed up and go test (TUG) with a concurrent cognitive task of counting
backwards and a motor task of carrying a meal tray in order to understand the role of
cognitive factors in gait-related balance control.
Significance: Exploring the influence of attention processes on gait may represent
an efficient way to- 1) improve the assessment of the falling risk among older adults, 2)
help in promoting healthy senile life by focusing on cognitive development, and 3)
improve post fall rehabilitation interventions by inclusion of cognitive tasks in addition to
improving musculoskeletal impairments.
iii
Methods: Community dwelling healthy older adult volunteers participated in a
timed get up and go (TUG) test while their movements were recorded by a 3D motion
analysis system and force plates.
Statistical Analysis: Multiple one-way repeated measures ANOVAs were
performed for each of the kinematic and kinetic variables to examine differences across
task conditions. Post hoc tests with Bonferroni corrections were conducted for significant
main outcome variables. T tests were performed to assess cognitive skills.
Results: The study successfully examined 15 healthy older adults between ages
65 and 88, scores of > 25 on MMSE test on their performance of dual task conditions in
an attempt to get an insight into their gait and dynamic balance control system. We found
that the most complex task situation of carrying the food tray and counting backward had
the most adverse effect on gait performance in healthy older adults that resulted in 24%
more time required to complete the TUG task, about 12% slowing of gait, 40% increase
in double support time and a 10% decrease in the generation of medio-lateral ground
reaction force, with conservation of cognitive task performance. However, balance
control parameters like center of mass excursion and velocity in the frontal plane
remained unaffected.
Conclusions: Gait is an attention-demanding task, and a concurrent cognitive or
motor task disrupts walking performance in community-dwelling older adults.
iv
Dedicated to my altruistic parents, Chitra and Sujoy Mukherjee who gave me the roots to
grow and wings to fly.
Acknowledgements
What a gratifying journey this was!
As I reflect back on the past few years, I am filled with emotions as to how
seamlessly I experienced a life changing transformation, both personally as well as
professionally. Concluding this educational journey was not just about receiving the
doctoral degree. It went beyond that. I became a proud mother to this amazing bundle of
joy (and energy!), Vivaan who enthuses me to do the best in whatever I do. Today, I take
pride in myself, my family, my professors, my colleagues, my school and Toledo in
supporting me, motivating me, and being there for me since the beginning.
Dr. Armstrong, you were the reason I got this privileged opportunity to pursue my
doctorate and start a life with my best ‘bud’ Desikan. Knowing you and Diane has been
an honor and inspiration to me and Desikan. We shall remain forever grateful to you.
My wonderful Kinesiology family is what I needed when I flew 8000 miles,
leaving the comforts of my home to come here and work for a brighter future. I have a lot
to thank you all for. You all made it worthwhile.
I miss my grandparents who I lost during this journey. I am sure you would be
proud of your granddaughter today. I love you both immensely and there is not a single
day I don’t think of you.
I am blessed to have Desikan as my strength and shadow, and a family who I love
dearly- my parents, awesome sister Anumita and husband Raunak, and my super in-laws.
vi
Table of Contents
Abstract .............................................................................................................................. iii
Acknowledgements ............................................................................................................ vi
Table of Contents .............................................................................................................. vii
List of Tables .................................................................................................................. xii
List of Figures .................................................................................................................. xiii
1
Introduction …….……………………………………………………………… ..1
1.1 Background ........................................................................................................1
1.2 Definition of old age and falls ..........................................................................2
1.3 Aging and changes associated with it ...............................................................2
1.3.1 Physical Changes ....................................................................2
1.3.1.1 Changes in gait …………………………………...………2
1.3.2 Social changes .........................................................................7
1.3.3 Cognitive changes ...................................................................8
1.4 Falls and aging ...................................................................................................9
1.4.1 Falls .........................................................................................9
1.4.2 Definition of falls for our research purpose .........................10
1.4.3 Incidence of falls ..................................................................11
1.4.4 Fall prevention approach.......................................................12
1.5 Dual/multi-tasking paradigm .........................................................................…9
vii
1.6 Research significance.........................................................................................9
1.7 Statement of purpose .......................................................................................17
1.8 Hypotheses ......................................................................................................19
2
Literature Review… ............................................................................................19
2.1 Introduction ......................................................................................................19
2.2 Balance control system ....................................................................................19
2.2.1 Posture, balance and its subsystems .....................................20
2.2.1.1 Aging effects on the sensory system ....................20
2.2.1.1.1 Proprioception and aging…………..21
2.2.1.1.2 Vision and aging ...............................22
2.2.1.1.3 Vestibular system and aging .............23
2.2.1.2 Central nervous system and aging ........................24
2.2.1.3 Musculoskeletal system and aging........................25
2.2.1.4 Integration of the different subsets of balance
control system and aging ......................................26
2.3 Attention .. .......................................................................................................29
2.3.1 Is gait automatic? ..................................................................30
2.3.2 Defining attention .................................................................30
2.3.2.1 Executive function (EF) ..........................................32
2.3.2.1.1 The normal anatomy and physiology of
executive function ….. .........................33
2.3.2.2 Executive function, gait and falls.............................34
2.4 Dual task paradigm ………………………………………………………… 35
viii
2.4.1 Dual task performance and falls ...........................................35
2.4.2 Dual task performance in healthy young and older adult
population…………………….. ...........................................36
2.4.3 Dual task performance in balance impaired older adult
population…………………….. ...........................................40
2.4.4 Dual task performance and training effects in healthy young,
older adults and balance impaired population ......................43
2.4.5 Dual task assessment protocols .............................................45
2.4.5.1 Timed up and go test (TUG) …………... 48
2.4.5.2 Center of mass measure (COM) ..............49
2.5 Conclusions ......................................................................................................51
3
Methodology……. ................................................................................................52
3.1 Participants .......................................................................................................52
3.2 Testing protocol ...............................................................................................53
3.2.1 Administering the TUG and MMSE tests and collecting
anthropometric data……………………………………….. 53
3.2.2 Data collection ......................................................................54
3.3 Instrumentation…………………………………………………..…………..56
3.3.1 Video data .............................................................................56
3.2.2 Reflective marker placement ................................................59
3.2.3 Force data………………………………………………….. 59
3.2.2 Cognitive task .......................................................................60
3.4 Statistical analysis………………………………………………..…………..61
ix
3.4.1 Dependent variables ..............................................................61
3.4.2 Independent variables ...........................................................62
3.4.3 Statistical tests…………………………………………….. .62
4
Results…….…………………………………………………………………….. 63
4.1 Subject description ...........................................................................................63
4.2 Temporal-spatial parameters ............................................................................64
4.2.1 Duration of task…..………………………………………...67
4.2.2 Gait speed..............................................................................68
4.2.3 Cadence…………………………………………………….69
4.2.4 Stride length ..........................................................................70
4.2.5 Step width………………………………………………….72
4.2.6 Double support time ..............................................................73
4.2.7 Swing time…........................................................................75
4.3 Kinetic-kinematic parameters ..........................................................................76
4.3.1 Total COM excursion in the frontal plane………………....77
4.3.2 Peak GRF in the frontal plane……………………………...78
4.3.3 Mean COM velocity in the frontal plane ..............................80
4.4 Cognitive parameters .......................................................................................81
4.4.1 Response accuracy………………………………………....82
4.4.2 Response timing ....................................................................82
5
Discussion…….….................................................................................................84
5.1 Introduction ......................................................................................................84
5.2 Evidence in support of hypothesis 1 ................................................................84
x
5.2.1 Temporal-spatial parameters……………………………….84
5.2.2 Conclusion ............................................................................92
5.3 Evidence in support of hypotheses 2 and 3.………………………………….93
5.3.1 Total COM displacement and velocity .................................93
5.3.2 Peak GRF in the frontal plane…………..………………….95
5.3.3 Conclusion ............................................................................97
5.4 Evidence in support of hypothesis 4……..…………………………………..98
5.4.1 Response accuracy and response timing ...............................98
5.4.2 Conclusion………………….…………..………………….99
5.5 Overall conclusions of the study ....................................................................100
5.6 Limitations of the study………………………………………….………....101
5.7 Future work…………………………………………………………………103
References ........................................................................................................................104
Appendices
A
IRB approved subject recruitment flyer ...............................................................122
B
Informed consent sheet ........................................................................................123
C
Data collection sheet ............................................................................................128
xi
List of Tables
1.1
Spatiotemporal gait characteristics in healthy young and elderly ...........................3
2.1
Most common risk factors for falls identified in elderly .......................................28
4.1
Subject characteristics of age, height and weight ..................................................64
4.2
Baseline performance scores of MMSE, TUG, and cognitive performance .........64
4.3
Temporal-spatial parameters across task conditions..............................................66
4.4
Kinetic-kinematic parameters across task conditions ............................................77
4.5
Cognitive parameters across task conditions .........................................................82
xii
List of Figures
1-1
Sagittal plane comparison of joint kinematics in elderly and young..………….....5
1-2
Frontal plane comparison of gait in elderly and young adults…………..………...7
1-3
Multifactorial intervention approach for fall prevention in elderly …..…..……..14
2-1
Schematic representation of components of balance control and functioning…..26
2-2
Illustration of different types of attention………………………………………..32
2-3
Illustration of different lobes of brains and their function……………………….33
3-1
Laboratory setup with cameras mounted on the wall ............................................57
3-2
Biomechanical visual 3d model of a subject with whole body COM……………58
4-1
Graph comparing duration of task across all task conditions…………………....68
4-2
Graph comparing gait speed across all task conditions .........................................69
4-3
Graph comparing cadence across all task conditions ............................................70
4-3
Graph comparing stride length across all task conditions .....................................71
4-3
Graph comparing step width across all task conditions .........................................73
4-3
Graph comparing double support time across all task conditions .........................74
4-3
Graph comparing swing time across all task conditions ........................................76
4-3
Graph comparing total COM excursion in frontal plane………………………...78
4-3
Graph comparing peak GRF in frontal plane .........................................................79
4-3
Graph comparing mean COM velocity in frontal plane ........................................81
xiii
Chapter 1
Introduction
1.1 Background
With the advances in technology in the past decade like smart phones, handheld
audio-video devices, global positioning systems (GPS) etc., mental processing involving
multi-tasking has involuntarily begun to occupy an enormous portion of our everyday
routine. As this has become increasingly widespread, significant concern has been
expressed about the potential detrimental effects of multi-tasking on function. A simple
example of the interference that this causes is the cognitive demands of cell phone use for
text messaging or talking while simultaneously driving (motor task). According to the
National Safety Council: Cell Phone Crash Estimate Model, in 2012 approximately
980,000 accidents have taken place due to interference of multi-tasking that involves the
effects of a cognitive task (using cell phones) on a motor task (driving).[1] While the
effect of multi-tasking on driving has received significant attention, there are many other
examples of multi-tasking that may be equally problematic. Some of these are clearly the
result of the attentional demands associated with many popular modern conveniences that
we have embraced. However, several others may simply be the result of the demands of
1
the increasingly busy lifestyle that many people have adopted. Hence, it has become
imperative to determine the influence of multi-tasking on aspects of our daily tasks that
are subject to the effects of multi-tasking..
All individuals appear to be susceptible to the negative influence of the competing
demands of multi-tasking. However, some sub-groups in the population may have factors
that interact with these demands in ways that exacerbate the negative impact multitasking can have on normal function. One sub-group where this may be of particular
concern is the elderly. It is well-know that the aging process results in decrements in both
cognitive and motor functions. However, the elderly are just as likely to encounter
situations that involve multi-tasking as their younger counterparts. As a result, it may
well be that aging factors play a prominent role in contributing to the likelihood that the
elderly will experience the negative consequences of multi-tasking. How and why this
occurs is not well known. Thus, it is essential to examine factors in aging that may
contribute to this problem.
1.2 Definition of ‘old’ age and ‘falls’
Old Age: There are several commonly used definitions of old age, but there is no
general agreement on the age at which a person becomes old. Calendar age is generally
used to mark the threshold of old age which assumes equivalence with biological age, yet
at the same time, it is generally accepted that these two are not necessarily synonymous.
At the moment, there is no United Nations (UN) standard numerical criterion, but the UN
agreed cutoff is 60+ years to refer to the older population.
2
1.3 Aging and changes associated with it
A number of physiological changes occur as we grow older. Aging in humans
refers to a multidimensional process of physical, psychological, and social change.
‘Normal/healthy’ aging is aging which occurs without disease. That is, there are a
number of physiological changes, that do not involve a pathological process, and, though
there may be bodily changes in the person, the person enjoys good function of mind and
body, and is able to live independently, and with a good quality of life.
1.3.1 Physical changes
Physical changes that are associated with aging are a reduction in muscle and
bone mass, which causes bones to be fragile, decreased joint ranges of motion (ROM),
slower nerve impulses resulting in slower reaction rate, decline in vision and hearing,
decreased lung capacity, increased susceptibility to infection and interrupted sleep to
name a few. [2, 3] Deterioration of proprioception or joint-position sense [4, 5] and a
progressive loss of vestibular hair cells and nerves [6] is also observed with aging. All of
these may contribute to the likelihood of falls and associated injury.
1.3.1.1 Changes in gait
It has been well established that the kinematics of gait in healthy, older adults
compared with healthy, young adults when walking at their preferred speeds, are
different.
As an extension of this, decline in gait speed also remains as one of the most
consistent age-associated changes [7,8]. Kerrrigan et al [9] (1998) demonstrated the effect of
aging on comfortable and fast paced gait in healthy elderly, seen in Table 1.1.
3
Table 1.1 Spatiotemporal gait characteristics in healthy young and elderly
population. Noticeable difference in gait speed in elderly is observed.
Young (n=31)
Elderly (n=31)
Temporal
Comfortable speed
Comfortable
Fast speed
Parameters
Mean ± SD
speed Mean ± SD
Mean ± SD
Velocity (m/s)
1.37 ± .17
1.19 ± .13
1.55 ± .20
Cadence (steps/min)
119 ± 10
119 ± 9
140 ± 17
Stride length (m)
1.38 ± .11
1.20 ± .12
1.33 ± .14
Double support (%)
23.8 ± 2.3
24.9 ± 2.8
23.5 ± 2.9
As can be seen, what older adults consider to be a comfortable gait speed is
considerably slower than that of a younger population. And, this decrease in speed is
primarily the result of a decreased stride length. Older adults also exhibit a shorter step
length, shorter relative swing phase time, and less range of motion at the hip, knee, and
ankle joints compared with young adults.[10, 11] These normal age related kinematic
changes in the body have an underlying effect on joint kinetics, primarily at the ankle
joint, expressed as a lower plantar flexor torque and power [7, 9]and possibly at the knee
and hip joints, expressed as reduced torque capacities at both these joints[9].
The study by Kerrigan et al. (1998) [9] further displayed how sagittal plane
kinematics and kinetics vary between young and old, when they walk at their preferred
vs. fast speeds, seen in Fig.1. Decreased extension ranges of motions are observed at the
ankle, knee and hip joints. The authors postulated that the observed reduction in hip
4
extension is associated with an overall increase in anterior pelvic tilt, or by kyphosis and
lumbar lordosis. Differences that persisted at both comfortable and fast walking speeds in
elderly (highlighted with circle in figure 1) were decreased peak hip extension, increased
anterior pelvic tilt, and decreased ankle plantarflexion and ankle power generation.
Differences that did NOT persist with fast walk (highlighted with rectangle in the figure
1-1) were hip, knee moment and knee power generation.
Figure 1-1 Sagittal plane joint angles, moments, and powers at hip, knee, and ankle for
young subjects at comfortable walking speed and for elderly subjects at comfortable and
fast walking speeds.
5
Figure 1-2, a study by Koi et al. (2010) [12] examined frontal plane gait under (1)
usual speed, (2) fast speed and (3) post- activity (fatigue) in 183 Baltimore Longitudinal
Study of Aging participants (mean 73 ± 9 years) who could walk unassisted. Their results
indicated that medial–lateral (ML) hip-generative mechanical work expenditure declined
with age and the rate of decline was steeper for walking at fast speed and post-activity
during hip extension compared with usual-speed walking. This decline was attributed to
lower angular speed and strength in hip abductor muscle group.
6
Figure 1-2 Exacerbated declines in gait speed (a), ML (medial lateral) ankle MWE
(mechanical work expenditure) in plantar flexion (b), ML hip MWE in extension (c) and
ML hip MWE in flexion (d) during challenging walking tasks (usual speed, fast speed,
fatigued walking). [12]
These normal age-related changes affect joint kinetics in the body, and it is quite
likely that they influence gait-related balance by altering center of mass (COM) motion in
the medio-lateral direction. It has previously been established that even healthy older
7
adults experience difficulty in controlling medio-lateral stability. [13] Since human gait
includes a considerable single support period, control of medio-lateral balance can be an
issue [7, 14] particularly while transitioning support from one limb to the other. Thus, it is
clear that older adults adopt gait patterns that may reflect both the physical and cognitive
changes that occur with aging. Further, it is likely that factors that may compromise
physical or cognitive functioning could have particularly detrimental consequences for
the elderly.
1.3.2 Social changes
Socially, older adults experience retirement as a series of losses, such as the loss
of a structured activity, loss or reduction of income, loss of status, and loss of a social
network. The death of a spouse is another common event among older people that
represents a loss intimacy and social support. While it may appear that social change and
physical function are independent, they are not. Social and other factors that affect
individual emotionally have been shown to influence many physiological processes.
These, in turn, may influence a wide range of functional characteristics. Thus, the social
changes that often accompany aging may well play a role in the changes in gait and
balance that are seen in the elderly.
1.3.3 Cognitive changes
As we age, our brain and nervous system go through natural changes. There is
loss in weight and volume of nerve cells. Waste products can collect in the brain tissue as
nerve cells break down, causing abnormal structures called plaques and tangles to form.
A fatty brown pigment called lipofuscin can also build up in nerve tissue.[15] A decrease
8
in the number of giant pyramidal cells within the motor cortex, a progressive loss of
neurons and depletion of neurotransmitters (e.g. dopamine) within the basal ganglia,
synaptic degeneration, blood flow reductions, and neurochemical alterations are also
prominent. [16]
These measurable changes in cognitive efficiency may lead to difficulties often
referred to as “benign senescent forgetfulness”, inefficiencies of executive functions like
difficulties in problem solving, multitasking, selective attention, and flexible thinking,
and slowed motor processing speed. Given these changes, it is not surprising that older
adults perform more poorly than young adults in a variety of cognitive tasks, including
perception, attention, and memory tasks.
In the elderly, one of the direct consequences of all of these factors in
combination is disruption of normal gait and as a result, an increase in the risk of falls.
1.4 Falls and aging
1.4.1
Falls
Falls are a significant problem in the elderly population, and as a result, have been
studied extensively. Recently, increased attention has been given to the degree to which
multi-tasking may influence the probability of falls among the elderly. However, falls
have been defined and reported in different ways. There is no universal consensus on the
definition of a fall. In 1987, the Kellogg International Working Group [17] on the
prevention of falls in elderly defined a fall as “unintentionally coming to the ground or
some lower level and other than as a consequence of sustaining a violent blow, loss of
9
consciousness, sudden onset of paralysis or an epileptic seizure”. Since then, many
researchers have used this definition or similar ones.
Falls occurring as a result of dizziness or syncope have been incorporated under a
broader definition of falls by many researchers, with varying focus of study. While some
consider the Kellogg definition appropriate for studies that aim at identifying factors that
impair sensorimotor function and balance control, many feel the need for a broader
definition that also addresses cardiovascular causes of falls such as postural hypotension
and transient ischemic attacks. [18]
The WHO [19] defines falls as “inadvertently coming to rest on the ground, floor or
other lower level, excluding intentional change in position to rest in furniture, wall or
other objects”.
The ANA–NDNQI [20] provides an all-inclusive definition:
“An unplanned descent to the floor (or extension of the floor, e.g., trash can or
other equipment) with or without injury. All types of falls are included, whether they
result from physiological reasons or environmental reasons”.
As per Zecevic et al.(2006),[21] “Older people tend to describe a fall as a loss of
balance and associate it with antecedents and consequences, whereas health care
professionals focus mainly on the description of the event”.
With such diverse and fragmented definitions, an operational definition of a fall
would be valuable for both research consistency and effective falls management and
prevention.
10
1.4.2
Definition of fall for our research purpose
For our purposes, we have defined ‘fall’ as an event where an individual
unintentionally comes in contact with ground as a result of loss of balance, irrespective of
physical injury, where the human balance control system cannot correct for the error in
time”. Loss of balance, preceding a fall, can result from intrinsic factors like divided
attention, errors in temporal-spatial judgment, or environmental factors like poor lighting,
bathtub without rails, ill-fitting shoes, loose carpet, unsafe stairs, but NOT related to use
of medication that triggers or causes falls (ex. antidepressants, sedatives, hypnotics) or a
preexisting medical condition like vertigo or related to malfunctioning of vestibular
functioning.
What we need to realize is that that falls among older adults are not a normal
consequence of aging; rather, they are considered a “geriatric syndrome most often due to
discrete multifactorial and interacting, predisposing (intrinsic and extrinsic risks), and
precipitating (dizziness, syncope) causes”. [22, 23]
1.4.3
Incidence of falls
“One out of three adults age 65 and older falls each year”. [24, 25] Falls are the
foremost cause of accidental death among older adults. [26, 27] They are also the most
common cause of nonfatal injuries and hospital admissions for trauma.[28]
Not all falls are fatal, but some have the potential to leave an individual with
devastating physical, psychological, and social consequences. Nonfatal falls often lead to
physical injuries like hip fractures, head trauma, reduced levels of activity, loss of
confidence, and changes in lifestyle and can increase the risk of early death. [29-31]
11
Although most incidences of falls involve multifactorial causes, it has been
shown that balance impairment is a major contributor to falling in older people.[26, 28-31]
Previous literature reports on the incidence of falls [32-35] indicate that about 50% of the
falls occur during some form of locomotion. Hence, it can be said that it is during
walking that we challenge our balance control system the most: during gait initiation and
termination, turning, avoiding obstacles , by altering step length, changing direction,
stepping over objects, etc., or when we accidentally bump into people and objects.
The problem of falls in the elderly population is clearly more than simply a high
incidence, as falls are highly prevalent in young children and athletes as well. Rather, it is
a combination of a high incidence coupled with a high vulnerability to injury in presence
of clinical diseases like osteoporosis and age-related physiological changes like slowed
protective reflexes that make even a relatively mild fall particularly dangerous. In
addition, recovery from fall injury is often delayed in older persons, which in turn
increases risk of subsequent falls through deconditioning. Another complication is the
post-fall anxiety syndrome, in which an individual down-regulates activity in a perhaps
overcautious fear of falling; this in turn further contributes to deconditioning, weakness
and abnormal gait and in the long run may actually increase risk of falls. Clearly, as age
progresses, concern for the negative consequence of falls increases. Thus, factors that
may relate to the probability of falling, such as the interaction between multi-tasking and
gait/balance, need to be thoroughly investigated.
12
1.4.4
Fall prevention approach
A fall can change a life. In an older adult, it is a major threat to independence and
quality of life. Hence, the importance of an effective fall prevention approach is
desirable. Rubenstein (2006) [36] in his study classified fall prevention interventions into
several broad categories: multidimensional fall risk assessment coupled with risk
reduction, exercise programs of various types, environmental assessment and
modification, multifactorial interventions, and institutional interventions. Although the
goal of preventing falls is common to each type of intervention, the approach taken by
each is different.
Regardless of the method, effective approaches to fall prevention include
multidimensional risk factor assessment with targeted interventions, exercise programs
(which include balance, strength and endurance training), and environmental assessment
and modification, as illustrated in Figure 1-3. Programs combining all of these
approaches seem to have had the strongest effects. Recent clinical practice guidelines
from the American Geriatrics Society, British Geriatrics Society, and American Academy
of Orthopedic Surgeons panel and other organizations have strongly advocated
preventive approaches using these three components. [37]
13
Figure 1-3 Multifactorial intervention approach for fall prevention in elderly. The
figure shows that not just exercise programs are quintessential to fall prevention, but also
environmental assessment/modification and multifactorial intervention approach. [37]
14
1.5 Dual/multi-tasking paradigm
While approaches to fall prevention indirectly reflect an understanding about the
role of the brain, only recently has the link between these factors and falling begun to be
studied. The information processing capacity of the human brain is limited. It has been
well established that the physiological, social and psychological factors associated with
aging further limit this capacity. And, in turn, this limitation can adversely affect
function in gait and balance. However, as discussed previously, we live in age when the
demands on the brains ability to process information are increasing. As an example, our
everyday life involves numerous situations in which a motor task and a cognitive task are
performed simultaneously, for example walking at home while talking on the phone,
walking down a street while mentally rehearsing a shopping list, crossing a road while
watching for traffic, driving an automobile while conversing, either on a mobile
telephone or with a passenger, watching television while having dinner and many more
such activities of daily living. All of these result in greater demands on the processing
capacity of the brain than any one of the activities individually.
To study the effects of increased demand on processing capacity, one of the
approaches that has been used, is that comprising dual-tasking. Dual-task methodology
requires an individual to perform two tasks - a task that will be assessed in terms of its
attentional demand (cognitive), while simultaneously performing an alternative task
(motor or cognitive), called as a secondary “probe task”. Vocal or manual reaction-time
tasks are frequently used secondary probe tasks.[38] These tasks consist of presenting a
stimulus (usually visual or auditory) that the subject responds to as rapidly as possible,
15
typically with a vocal or manual response. Both tasks are then performed together,
allowing the performance of each to be measured and compared with single-task
performance.
Assessment methods incorporating dual-task paradigms appear to be helpful in
revealing the effect of age or disease on the allocation of attention to postural tasks. As
such, they may be particularly sensitive in predicting fall risk and/or in evaluating
outcomes of fall interventions in older people. [39] As mentioned earlier in this chapter
50% of the falls occur during some form of locomotion. Thus it becomes logical to assess
older adults (who are a high risk population for falls), through the use of dual task
methodologies, and in a laboratory set up where the biomechanical mechanisms affected
by dual-tasking can best be assessed. This approach has the potential to add significantly
to the chances of identifying ‘fallers’, as well as making it a highly important aspect of
mobility research.
1.6 Research Significance
Previous research has established that falls are a common problem among
the elderly. Additionally, research has identified many factors associated with the aging
process that predispose the elderly to an increased risk of falling. It would appear that
such predisposition may well be influenced by factors that increase the demands that are
placed on any of the systems involved in the maintenance of balance. Dual-tasking is an
example of one of these factors. Carrying out more than one task at the same time is
commonplace in everyday life, for example driving an automobile while conversing,
16
either on a mobile telephone or with a passenger, crossing a street while rehearsing a
grocery list, watching television while having dinner and many more such activities of
daily living. As dual-tasking during walking is common, assessment of this paradigm
may provide insight into factors that contribute to falls in the elderly. Exploring the
influence of attention processes on gait might represent an efficient way to- 1) improve
the assessment of the falling risk among older adults, 2) help in promoting healthy senile
life by focusing on cognitive development, and 3) improve post fall rehabilitation
interventions by inclusion of cognitive tasks in addition to improving musculoskeletal
impairments, integrating sensory input for balance, and promoting gait activities.
1.7 Statement of Purpose
The purpose of this study is to investigate the effects of dual tasking on
measures of gait and dynamic balance in a population of healthy elderly individuals
through assessment of frontal plane COM displacement and velocity, temporal gait
characteristics, verbal response time and accuracy during a timed Get-Up-And-Go test
(TUG) test with a concurrent cognitive task of counting backward and a concurrent motor
task of carrying a meal tray.
17
1.8 Hypotheses
The research task involved in this study will involve four levels of complexity:
W (simple - TUG),
WT (more complex motor- TUG with carrying a simulated food tray),
WC (more complex cognitive - TUG with counting backwards by 3),
WCT (most complex - TUG with carrying food tray and counting backward by 3).
Volunteers were instructed with equal prioritization to both tasks – motor and
cognitive.
1.
It is hypothesized that temporal gait characteristics including cadence, step
length and stride length will be decreased with increasing task complexity.
Double support time, and step width will increase with increasing task
complexity.
2.
Center of mass displacement in the frontal plane will increase with increases
in task complexity.
3.
Center of mass velocity in the frontal plane will increase with increases in
task complexity.
4.
Verbal response time and accuracy will decrease with increasing task
complexity.
18
Chapter 2
Literature review
2.1 Introduction
This chapter provides a comprehensive overview of the components of the
systems for balance control in humans, and the methods by which balance control is
measured. Additionally, the assessment of dual/multi-tasking in young, old and balance
impaired population is discussed. And finally, the importance of attention/cognition in
today’s increasingly complex world is examined.
2.2 Balance control system
From a simple physical task like reaching for a pen to a highly complex activity
like skiing, all forms of normal human motion involve well-coordinated movements, and
a dependence on the maintenance of balance. Rarely do movements occur as a result of a
single muscle contracting; practically all of our body motions involve multiple muscles
working in sequence or collectively at once. For example, normal walking is produced
through activation of virtually all the muscles of the legs, at different intensities and in a
well-defined sequence. Similarly, reaching movements are the product of multiple
muscles of the arm and shoulder, while grasping and manipulating objects requires the
19
addition of muscles of the forearm, hand, and fingers. These contraction produce joint
torques, occurring in the proper sequence and of an appropriate extent, such that the
resulting motion is smooth, straight, and directed to the goal of interest. Whether these
movements involve the upper body or lower body, or a combination of both, all require
the maintenance of balance. Thus, the goal directed movements that are essential to
normal functioning are superimposed on a structure of muscle contractions and resulting
joint torques that provide a balanced platform from which these movements can occur.
Thus, control of balance is an essential task of the neuromuscular systems, and when
impaired in any way, disrupts all of the associated movements.
How do we humans achieve this?
2.2.1 Posture, balance and its subsystems
The concepts of balance and human movement are integral and, in terms of
rehabilitation, fundamental to a wide variety of clinical specialties. Health professionals
use the term ‘balance’ under many different contexts in the clinical field. Balance is often
used to imply stability and postural control. Like ‘falls’, there is no universally accepted
clinical definition for human balance, or its related terms.
From a biomechanical standpoint, “Balance is the ability to maintain the body’s
center of mass over its base of support” [40, 41] Similarly, it has been described as “the
ability to maintain equilibrium and orientation in a gravitational environment”.[42] And,
Maki and McIlroy (1997) [43] described ‘balance control’ as “the ability to regulate the
relationship between the line of gravity and the base of support during activities of daily
life”.
20
The task of postural control involves maintenance of the body’s position in space
for the dual purposes of stability and orientation.[41, 44] Biomechanically, this describes
orientation of any body segment relative to the gravitational vector. It is often expressed
as an angular measure from the vertical. [45]
The phrases postural control or balance control can be used interchangeable as a
description for the task of maintaining or returning the body close to a state of static or
dynamic equilibrium.[46] Such control is achieved and maintained by a complex set of
sensorimotor control systems that include: 1) sensory input from vision (sight),
proprioception (touch), and vestibular input (motion, equilibrium, spatial orientation); 2)
integration of input within the central nervous system (CNS) and 3) appropriate responses
to the integrated signal by the body's musculoskeletal system. [47] When all of the
involved systems are intact and functioning, the result is normal balance control.
Anything that disrupts these systems in any way has the potential to compromise balance
and impair the associated movements. While this may occur as a result of injury to any
of the involved structures, it is also a direct consequence of the aging process. Thus,
many elderly individuals experience diminished balance simply as a result of changes to
the balance control systems that are a result of growing older.
2.2.1.1 Aging effects on the sensory systems
2.2.1.1.1 Proprioceptors and aging
The somatosensory system provides information related to body position and
information about our environment (e.g. temperature, contact surface condition, pressure
distribution, presence of any noxious stimuli) by proprioceptors. The proprioceptive
21
receptors are located in muscles, tendons and joints, and they provide information about
the position of the limbs and the body and the distension of the respective muscles.
Proprioceptors include muscle spindles (type Ia and II), Golgi tendon organs (Ib) and
joint receptors.
Joint receptors include free nerve endings, pacinian corpuscles, and Golgi-type
receptors. They are responsible for detecting changes in joint angle and pressure that
compress and distort the joint capsule receptors. The muscle spindles (enclosed by the
joint capsule) give information about the changes in muscle length, and they can be
activated by stretching the muscle. Golgi tendon organ (located in the tendons that attach
muscle to bone) provides information about muscle tension and are best stimulated when
muscles contract.
Proprioception is particularly vital during changes in position, walking on uneven
surfaces, and when other senses are impaired. Though changes in peripheral nerves have
not been established, peripheral neuropathies associated with diabetes and vitamin B12
deficiency are common among the elderly. [48] With such neuropathy, muscle response to
perturbation can be significantly delayed, which disrupts normal adaptation to factors
causing instability.
2.2.1.1.2 Vision and aging
The visual system has also been categorized as a proprioceptive system because it
not only provides information about our environment but also about the orientation and
movement of our body, and because of this it is referred to as “exproprioception” [49] The
visual system (eyes) monitors where the body is in space (i.e. upside down, right-side up,
22
etc.) and also the directions of motion. Vision provides necessary information to the brain
about our relationship to the environment. As we move and see how objects in our world
are changing, our brain calculates our body's relationship to those objects. The best
example of this is looking over a cliff; we feel as though we are falling, even though our
bodies are stable.
Older adults and children rely on input from their visual system more than young
adults, with unstable older adults showing an even greater reliance on visual input in
order to maintain their balance. [50] Nashner and Berthoz (1978) showed that enhancing
the visual input reduced the sway amplitude, while reducing vision increased the final
amplitude.[51] Thus, the structural changes in the aging eye and the decline in visual field,
acuity and contrast sensitivity is thought to be a contributing factor to additional postural
imbalance.
2.2.1.1.3 Vestibular system and aging
The vestibular apparatus, located in the inner ear, consists of two types of sensorstwo otoliths organs and three the semicircular canals, and provides information about
head movement, independent of the visual system. Otoliths sense linear acceleration (i.e.,
gravity and translational movements. The semicircular canal consisting of three fluid
(endolymph) filled passages present in the three planes – sagittal, frontal and horizontal,
provide information regarding rotation of the head, or angular/rotational acceleration.
Since vestibular responses are associated with eye movements and hearing, they
contribute to visual and auditory processing as well as to balance. The vestibular system is
syaptically linked to the extrapyramidal system. Thus, persons with extrapyramidal
23
neurodegenerative disorders frequently also have problems with balance and may
experience frequent falls.
Like vision and proprioception, the vestibular system also declines with age, with
an approximately 40% reduction in sensory cells for adults over the age of 70. [52] Agerelated changes to balance also occur as a result of the accumulation of minute
calciferous granules within the vestibular system. [27] Along with vestibular deficits that
diminish accurate balance-related sensory input, many older adults will also experience
symptoms of dizziness, which can also play a significant role in imbalance.
2.2.1.2 Central nervous system and aging
Multiple regions of the central nervous system (CNS), which consists of the
spinal cord and the brain, take part in controlling posture. Afferent information to the
cortex comes mainly from the thalamic nuclei, which transmit information from the
spinal cord, basal ganglia, and cerebellum and from the parietal and frontal areas of the
cortex. The motor commands are sent to the muscles via the pyramidal and
extrapyramidal systems. The pyramidal cells, with their connections to the premotor and
parietal cortex, transmit information to the spinal motorneurons and interneurons, which
control voluntary movements. The extrapyramidal system indirectly controls motion. It is
also referred to as the indirect activation pathway of motor functions. Primarily, the
extrapyramidal system is involved in maintaining equilibrium, coordination, posture,
muscle tone, and reflexes. These nuclei include the substantia nigra, caudate, putamen,
globus pallidus, thalamus, red nucleus and subthalamic nucleus. All of these nuclei are
24
synaptically connected to one another, the brainstem, cerebellum and the pyramidal
system.
While aging affects the CNS in many ways, with respect to balance control, the
collective effects of aging on the CNS result in slower reaction times. There is unanimity
about the decrease in the volume of gray matter with progressing age. Additionally,
neuronal loss with age in different areas like frontal and temporal lobes, subcortical areas
and hippocampus has been reported, [53] which manifests in slower integration of afferent
as well as efferent processing by the CNS. Functionally, this impairs the normal
sequencing of muscle contractions, resulting in a delayed response to balance
perturbations. Undoubtedly, this is a major contributing factor in falls.
2.2.1.3 Musculoskeletal system and aging
After receiving the afferent inputs from various sources, the central nervous
system makes an action plan, and this plan is executed via the musculoskeletal system.
Collectively, the musculoskeletal system is made up of the body's bones (the skeleton),
muscles, cartilage, tendons, ligaments, joints, and other connective tissue that supports
and binds tissues and organs together.
While aging appears to effect the musculoskeletal system in many ways, of
particular significance is that aging is associated with decreases in muscle cross sectional
area and the volume of contractile tissue within that cross sectional area. [54] As muscle
cross-sectional area correlates very highly with contractual force, this means that elderly
individuals typically have lost a significant amount of their force generating potential.
Obviously, this impairs movement potential, and as such, significantly impacts balance
25
control. Additionally, with concomitant pathologies like osteoarthritis, joint pains etc. in
older adults, falls are a common finding.
2.2.1.4 Integration of the different subsets of the balance control system and aging
The control of balance requires the integration of information from multiple
sensory and motor systems by the CNS. Balance receptors in the inner ear (the vestibular
system) provide information to the CNS about head and body movements through the
vestibulo-ocular reflex (VOR). The eyes (visual system) provide input regarding the
body's orientation within the environment and about motion within the environment. The
position and motion sensors of the muscles and joints, and the touch receptors of the
extremities (proprioceptive system) send signals regarding bodily position, particularly in
relation to the support surface. The CNS integrates all this data, determines the body's
spatial orientation, and sends appropriate neural impulses that stimulate reflexive actions
in the musculoskeletal system to cause the body to react as necessary to maintain balance.
26
Sensory input
Proprioception
• From joints,
muscles, skin
Vision
• Eyes
Vestibular
• From inner ear
CNS integration
Cerebellum
Coordinates and
regulates
posture,
movement and
balance
Cerebral cortex
Thinking,
memory
Brainstem
• Integrates and
sorts sensory
information
Motor response
Vestibular ocular
reflex
Motoneurons:
1) to control eye
movement and
2) postural
adjustment
Figure 2-1 Schematic representation of the componenets of balance control
system and its functioning.
With aging, much of the changes in musculoskeletal function in the elderly can be
attributed to changes in the neuromuscular system, which has a role of integrating
sensory information and maintaining postural control. The musculoskeletal system is
considered the effector system which maintains posture and controls movement, with the
nervous system planning and setting posture based on sensory input. [55] Impairments to
the sensory systems can affect the way our central nervous system is able to integrate
information about our environment. Hence, aging and impairments in any of the
components of the balance control system adversely affects balance.
27
Ganz et al. (2007) summarized findings of 15 studies that considered gait or
balance abnormalities and the risk of falling. [56] In 10 of 15 studies, older people with
impaired gait or balance had an increased risk of falls. The systematic review by
Rubenstein and Josephson (2002) assessed impacts of several factors on the risk of
falling. [57] Table 1 summarizes the findings of their review. Gait deficits were related to
falls in 10 of 12 studies and balance deficits in eight of 11 studies. Muscle weakness was
also strongly associated with falls.
Table 2.1 The most common risk factors for falls identified in 16 studies, findings
are based on univariate analyses. Adapted from Rubenstein and Josephson (2002) [57]
Risk factor
Significant / Total a
Mean
RR / OR b (range)
Muscle weakness
10 / 11
4.4 (1.5 to 10.3)
History of falls
12 / 13
3.0 (1.7 to 7.0)
Gait deficit
10 / 12
2.9 (1.3 to 5.6)
Balance deficit
8 / 11
2.9 (1.6 to 5.4)
Use of assistive device
8/8
2.6 (1.2 to 4.6)
Visual deficit
6 / 12
2.5 (1.6 to 3.5)
Arthritis
3/7
2.4 (1.9 to 2.9)
Impaired ADL
8/9
2.3 (1.5 to 3.1)
Depression
3/6
2.2 (1.7 to 2.5)
Cognitive impairment
4 / 11
1.8 (1.0 to 2.3)
Age > 80 years
5/8
1.7 (1.1 to 2.5)
a Number of studies with significant relative risk ratio or odds ratio / total number of studies addressing
each risk factor, b Relative risk ratios or odds ratios calculated for studies
28
Thus, we now know that aging adversely affects not one, but multiple
components of the human balance control system. Extensive research has estabilished the
adverse effects of sensory and musculoskeletal system declinefrom aging. Of more
recent interest, in an attempt to identify fallers amongst the elderly population, is another
factor that appears to have significant influence on the maintenance of functional balance
control; the cognitive influence. In the past, the influence of cognitive factors on balance
has recevived relatively little attention. It was believed that, since balance control is
largely reflexive in nature, cognitive factors had little infleunce. However, recent
research suggest otherwise. In particular, examining the effect of attention on balance
control, and specifically, how this may be infleunced by simlutaneously performing a
motor or a cognitive task has become an area of considerable interest in today’s mobility
research.
2.3 Attention
Since many falls in older adults occur while simultaneously balancing or walking
and performing a secondary task (such as engaging in conversation or carrying an object),
the examination of how attentional demands of secondary tasks affect balance control in
gait is a critical research area. [44, 58-60] Beyond this, as attention-demanding technologies
such as cell phones, personal music players, and navigation systems have come to
pervade everyday behavior, the necessity of understanding the impact of this on balance
control and motor function has become increasingly important.
29
2.3.1 Is gait automatic?
Historically, researchers have assumed that gait and postural control is a reflexive
and highly automated task that occurs in response to sensory and visual information,
requiring minimal, if any, cognitive processing or attentional resources. A growing body
of the literature, however, has provided evidence supporting the notion that postural
control requires a significant amount of attention and thus is not solely automatic. [61-65]
Research has established that walking, or even maintaining static balance,
requires more cognitive resources in late adulthood than at younger ages due to declining
visual and auditory acuity and reduced muscle strength and joint flexibility. This aginginduced integration of motor functioning with cognition makes it particularly difficult for
older adults to master situations in which a cognitive and a motor task must be performed
concurrently [66-71]
Thus dual-tasking, which is a common component of normal functioning, creates
a significant challenge for aging adults. Additionally, not to be overlooked, is the fact
thatyounger adults too suffer significant performance losses when dual-tasking [72, 73]
2.3.2 Defining attention
In the context of this discussion, attention in human performance is defined as
"the conscious and non-conscious engagement in the perceptual, cognitive and or motor
activities before, during, and after performing a skill” by Magill (2001). [74] Magill (2007)
goes on to state that “the human information processing system includes limitations in the
number of these activities that can be performed simultaneously". Thus, it is widely
accepted that humans have a finite capacity for attending to multiple sources of sensory
30
input. And, it has been suggested that performance on motor tasks may be adversely
affected by excessive attentional demands.
Attention can be classified into separate functions, including focused or selective,
sustained, divided and alternating, although these distinctions are somewhat artificial, as
illustrated in fig. 2.3.2. [75] Selective attention, which enables filtering of stimulus
information and suppression of distractors, is commonly referred to as “concentration.”
[76]
Sustained attention refers to the ability to maintain attention to a task over a period of
time. [76] Divided attention refers to the ability to carry out more than one task at the same
time and alternating attention refers to rapid shifting of attention from one task to
another. [76] Here, we focus primarily on divided attention. This type of attention plays an
important role in walking in multitasking and changing situations, serves as a common
tool for examining the attentional demands of various tasks, including walking, and has
clinical implications for fall risk.
31
Figure 2-2 Illustration of different types of attention
2.3.2.1 Executive function (EF)
Attention may be considered a specific example of EF. [77] The term executive
function (EF) is used as an umbrella term for various complex cognitive processes and
sub-processes. It refers to a variety of higher cognitive processes that use and modify
information from many cortical sensory systems in the anterior and posterior brain
regions to modulate and produce behavior. [76, 78, 79] These integrative functions include
both cognitive and behavioral components that are necessary for effective, goal-directed
actions and for the control of attentional resources, which are at the basis of the ability to
manage independent activities of daily living (ADL). [76, 77]
32
2.3.2.1.1 The normal anatomy and physiology of executive function
EF is traditionally associated with the frontal lobes and related brain networks, as
illustrated in fig. 2.3.2.1.1. The frontal lobes govern a wide variety of functions, including
awareness, insight, judgment, cognitive flexibility, rage, apathy, attention, fine motor
initiation, planning, and behaviors. The area of the prefrontal lobe and, in particular, the
dorsolateral prefrontal cortex (DLPFC, Brodmann’s area 9) and the cingulate cortex (e.g.,
the anterior cingulate: ACC, Brodmann’s areas 24, 32) have been related to the cognitive
aspects of EF. [16, 76, 77]
Figure 2-3 Illustration of different lobes of brains and their functions.
33
Many subcortical (lower brain) regions are wired to cortical areas (higher areas of
the brain) through the frontal lobes. Damage to the frontal lobes therefore affects not only
the functions that are governed by the frontal lobes, but also the connections of other
brain regions that run through this area. Patients with frontal damage frequently display
impairments in cognitive functions attributed to EF, although activation of other brain
areas, such as the parietal lobe, association areas and subcortical areas, including the
limbic areas, are also attributed to EF. [16, 76, 77, 80] In general, the anterior parts of the
frontal lobes are involved with aspects of self-regulation, such as inhibition and selfawareness, whereas the dorsal parts are involved with reasoning processes.
The frontal lobes are apparently highly susceptible to age-associated changes. [81,
82]
These include lesions of diffused white matter, which might affect fronto-striatal
circuits and cause, among other things, impairment in EF.
2.3.2.2 Executive function, gait and falls
In the InChanti study by Ble et al. (2005), over 900 non-demented older adults
(mean age 74.6 +/- 6.7 years, MMSE 25.5 +/- 2.8) walked at a self-selected speed and at
a fast speed over an obstacle course. [83] Their goal was to examine the relationship
between EF and performance on motor tasks of varied attentional demands. The authors
concluded that EF is critical in complex gait situations, a finding supported by many
other studies. [84, 85]
Falls risk has also been associated with age-related changes in the prefrontal
cortex and other brain regions that control EF. [86, 87] [88] Thus, it would appear to be likely
that attentional factors that influence EF play an important role in balance control,
34
influencing both functional gait and risk of falls. The research protocol typically used to
study the influence of multiple attention demanding tasks on balance control is the dual
task paradigm.
2.4 Dual task paradigm
Dual-task methodology is a testing model that intentionally requires a person to
simultaneously perform more than one task. As dual-tasking during gait is common,
assessment of this paradigm is highly important for mobility research.
2.4.1 Dual task performance and falls
Previous research has shown that difficulties with dual-task performance are
associated with a history of falls, [64] [89] and risk of future falls in institutionalized, [90, 91]
and community-dwelling [60] older adults.
A pioneering study by Lundin-Olsson et al. (1997) demonstrated that people who
stopped walking when talking had an increased tendency to fall. [90] This tendency
reflects the perception by these individuals that their stability was diminished when
engaged in the dual tasks of walking and talking. In conclusion, the authors suggested
that the tendency to stop walking when talking can be used as a predictor of falls in older
adults.
Faulkner et al. (2007) examined the relationship between changes in dual-task gait
performance and a history of recurrent falls. [92] They found that walking more slowly
while performing a visual-spatial decision task is associated with higher odds of recurrent
fall history.
35
Melzer et al. (2007) also found that the voluntary step execution test under dualtask conditions can identify older adults who are at risk for falls. Participants who spent
longer time (≥1,100 ms) on a stepping task under dual-task conditions had 5 times greater
falling risk than those who spent less time on the task. [93]
Research suggests that many falls in balance-impaired older adults occur not
when they are simply walking, but when they are walking and simultaneously performing
a secondary task, such as talking or manipulating an object. It has been hypothesized that
falls are not due to balance deficits in isolation, but to the inability to effectively allocate
attention to balance in multi-task conditions. [44, 63] A growing body of research on
attentional demands and posture suggests that the requirement for attentional resources
varies as a function of the postural task, the age and balance abilities of the subjects.
Assessment methods incorporating dual-task paradigms appear to be helpful in
revealing the effect of age or disease on the allocation of attention to postural tasks and
may be sensitive in predicting fall risk and/or in evaluating outcomes of fall interventions
in older people. [39] Research has also shown that interventions designed to improve dualtask balance performance have the potential to reduce falling rate and functional decline
and hence, they are a critical health care need. [94, 95]
2.4.2 Dual task performance in young and healthy older adult population
Researchers have examined the interaction between gait and attention under
different conditions including: introduction of external perturbations, manipulation of the
difficulty levels of the cognitive tasks, and directing the focus of attention to the
performance of either gait or the concurrent task separately or concurrently.
36
A cross-sectional, exploratory study by Hall et al. (2011) [96] examined 77
community-dwelling older adults with a mean (SD) age of 75.5 (5.8). The motor tests
used in the study measured strength (force-generating capacity), gait speed, and static and
dynamic balance. The cognitive abilities test assessed psychomotor and perceptual speed,
recall and working memory, verbal and spatial ability, and attention (sustained, selective,
and divided). The authors found that regardless of the cognitive task, participants walked
slower under dual-task conditions than under single-task conditions. Increased cognitive
task complexity resulted in greater slowing of gait. Another study by van Iersel et al.
(2007) also demonstrated the same effect of cognitive tasks on walking. [97] The authors
studied the effect on balance of 3 different cognitive dual tasks performed while walking
in 59 physically fit elderly people (mean age, 73.5y). They measured stride length and
time variability on a GAITRite® walkway. The results observed were decreased gait
velocity (1.46 to 1.23m/s), increased stride length (1.4% to 2.6%), and time variability
(1.3% to 2.3%). After standardization for gait velocity, the dual tasks were associated
with increased body sway (111% to 216% of values during walking without dual task)
and increased stride length and time variability (41% to 223% increase,). They concluded
that in physically fit elderly people, cognitive dual tasks influence balance control during
walking directly as well as indirectly through decreased gait velocity and this decrease in
gait velocity may be a strategy of walking under difficult circumstances.
In contrast to the elderly, young and middle aged adults do not appear to be as
readily affected by the challenges of dual tasking. A study by Hollman et al. (2007) [98]
examined whether gait velocity and stride-to-stride variability in gait velocity differed in
37
older adults compared with middle-aged and younger adults during normal and dual task
walking conditions. Twenty older adults (mean age=81 years), 20 middle-aged (mean
age=48 years), and 20 young adults (mean age=25 years) participated in the study. Gait
parameters were quantified with GAITRite® instrumentation. In the dual task condition,
participants spelled five-letter words in reverse while walking across the walkway.
Across groups, gait velocity was slower (p<0.001) and stride-to-stride variability in gait
velocity was greater (p = 0.001) in dual task walking. Older subjects walked more slowly
than did middle-aged and younger subjects and the difference in gait velocity was
greatest in the dual task condition (p < 0.05). Variability in stride velocity was increased
in older subjects compared with middle-aged and younger subjects (p < 0.05).
Additionally, in older subjects, impaired walking performance was associated with
impaired cognitive performance in dual task walking. The authors concluded that gait
changes observed in dual task walking characterize decreased gait stability and indicate
that cognitively demanding tasks during walking have a destabilizing effect on gait and
may place older people at a greater risk of falling.
A contradictory finding by Beauchet et al. (2005) on 49 healthy young adults
(mean age 24.1 ± 2.8 years) in a study comparing walking alone and walking with
simultaneous backward counting revealed that only certain aspects of walking are
attention-demanding in young adults. [99] Their results showed that dual tasking (walking
and counting backwards) caused a small decrease in stride velocity. Stride length
parameters however did not change significantly between both walking conditions. They
38
concluded by saying that young adults require minimal attention for the control of the
rhythmic stepping mechanism while walking.
An important question that has evolved from these studies is that involving the
uniqueness of elderly gait. Aging is associated with a multitude of factors that have the
potential to influence walking. Does this result in unique patterns of gait adaptation
among the elderly, or is their gait simply a slowed version of what is seen in younger
adults with the same patterns of adaptation? Shkuratova et al. (2004), in an attempt to
answer this, examined gait in 20 healthy older subjects (71.5 ± 5.03 years) and 20 healthy
young subjects (25.25 ± 5.95 years). [100] Changes in gait performance in response to
perturbations to balance were quantified for (1) straight line walking at preferred speed,
(2) straight line walking at fast speed, (3) figure-of-eight walking at preferred speed, and
(4) figure-of-eight walking while performing a secondary motor task. Multivariate
analysis of variance showed a significant interaction between age and speed when
balance was perturbed by requiring subjects to change from walking at a preferred speed
to a fast speed. And, this occurred because older people did not increase their cadence or
stride length as much as the young adults. The results did not show statistically
significant interactions between age and turning conditions or age and dual task
conditions, although older people walked more slowly and with shorter steps when
turning or performing a secondary task. They concluded by saying that balance strategies
during gait are task specific and vary according to age. In response to challenges to
balance imposed by the requirement to change from preferred to fast walking, older
39
people did not increase their speed and stride length to the same extent as did younger
adults. This was thought as possibly a strategy to maintain stability in older adults.
We know that the requirement for attentional resources in dual-task conditions
increases in older adults. In some studies this has been demonstrated as a reduction in the
performance of the secondary cognitive task, specifically an increase in reaction time. [44,
63]
Other studies have reported a decrement in the primary task, either that of postural
control, [101] or gait. [102] Regardless, it appears that the elderly are less able than their
younger counterparts to accommodate to increases in the attentional demands of on-going
motor and cognitive tasks. And, that this typically results in decrements in performance.
2.4.3 Dual task performance in balance impaired older adult population
Several studies have used a dual task paradigm to study the effects of a concurrent
task on gait in individuals with Parkinson’s disease (PD). [103-105] Camicioli et al. (1998)
examined the effects of a simultaneous verbal fluency task on gait in individuals with PD
with and without freezing of gait. [103] In this study 19 patients with PD: 10 subjects with
freezing (PD-F) and nine without freezing (PD-NF) participated, with 19 gender and age
matched healthy control subjects. Gait was measured while subjects walked in one
direction, turned and returned to the starting point at a self-selected pace. The number of
steps and the time in seconds to walk the total 30 feet was recorded. If patients froze
while walking, this time was included in total walking time. The secondary task involved
audibly reciting as many male names as possible while performing the gait task. The
results indicated that the number of steps and time taken to walk 30 feet was significantly
40
different among the three groups. PD-F patients took significantly more steps (mean
change: 4.2 ± 4.6 steps) and were slower (mean change: 2.0 ± 1.4 s) than either the PDNF (0.11± 1.62 steps, 0.44± 1.51 s) or the control groups (1.53 ±1.54 steps, 1.53± 2.04 s).
While the PD-NF group took more steps than the control group, they were not
significantly slower. Interestingly the authors note that antiparkinsonian medication
improved gait parameters in the PD-F patients to the level of the PD-NF patients, but did
not influence the dual task effect. They suggest that PD-F patients are more dependent on
attention when walking than PD-NF patients or control subjects. The authors conclude
that PD patients with freezing may have additional frontal attention deficits that interfere
with compensation during simultaneous task performance. These frontal deficits may
form the basis for freezing of gait in PD.
Bond and Morris (2000) investigated the effects of a motor task with two levels of
difficulty on gait in 12 subjects with PD and 12 healthy controls matched for age, gender,
and height. [105] Subjects performed a 10-m gait task while walking: (1) freely; (2) while
carrying a tray; and (3) while carrying a tray with four plastic glasses on it. Gait
measurements included: gait speed, stride length, cadence and the proportion of walking
cycle spent in double limb support. For all subjects with PD, experiments were performed
1 h after the last dose of medication. Results found that PD patients were significantly
slower in the single versus walking with tray and glasses condition (but not tray alone),
while there was no effect of either dual task on gait speed in the control group. In
addition, there was a significant reduction in stride length from free walking to walking
with tray and glasses in the PD but not the control group. Finally there was no effect of
41
dual task on cadence or double support in either group. The authors conclude that gait in
subjects with moderately severe PD is relatively unaffected by concurrent performance of
a relatively simple second task; however is markedly affected by performance of more
complex attentionally demanding tasks. They go on to suggest that subjects with PD have
an overreliance on cortically mediated attentional mechanisms when executing
movements because of defective basal ganglia function.
Pertersson et al. (2007) and Manckoundia et al. (2006) found that balance
performance under dual-task conditions can be used to identify people with Alzheimer's
disease. [106, 107] Subjects with Alzheimer’s disease were adversely affected by motor and
cognitive task independently as well as in combination. This suggests that, in these
patients, any increase in attentional demand can cause decrements in motor performance.
In several studies it has been demonstrated that older adults with clinical balance
impairments either stop walking completely [90] or take a longer time to complete a gait
task when they are challenged with an additional secondary task. [44] Shumway-Cook et
al. (2000) in their study found further evidence to show that competition for attentional
resources plays a role in instability and falls in balance-impaired elderly. [44] They
reported that as sensory conditions became more difficult, balance-impaired older adults
who had been able to maintain stability in a single task context, lost balance and had to be
caught to prevent a fall
It is evident from these studies that researchers have begun to recognize the
importance of factors that influence attention in studying gait and balance, and that dual
task methods hold great promise as a methodology for gaining insight into the role of
42
attention in gait and in predicting future fall As an extension of this, researchers have also
begun to examine the relationship between attentional demands and the control of posture
and gait during recovery of function and the rehabilitation process. Thus, a clinical
question of importance to rehabilitation is can dual task training have a positive outcome
in older adults?
2.4.4 Dual task performance and training effects in healthy young, older adults and
balance impaired populations
Several studies have shown the positive effect of task-oriented training on balance
and gait functions in a wide variety of populations including older adults [50, 108] and
individuals with stroke. [109, 110]
Pellecchia (2005) demonstrated that dual-task training was superior to single-task
training in improving dual-task balance performance in healthy young adults (aged 1846 years). [111] The author examined the hypothesis that following dual-task training, a
concurrent cognitive task would not amplify postural sway. Participants (N = 18) were
assigned to no-training, single-task training, or dual-task training groups. Single-task
training consisted of 3 sessions in which the postural task, quiet standing on a compliant
surface, and the cognitive task, counting backward by 3s, were practiced separately.
Dual-task training consisted of 3 sessions of concurrent practice of the cognitive and
postural tasks. After training, performance of a concurrent cognitive task increased
postural sway in the no-training and single-task training groups but not in the dual-task
training group. The results of the study suggested that dual-task practice improves dualtask performance.
43
Li et al. (2010) examined whether healthy older adults who completed five
sessions of nonmotor cognitive dual-task training would show significant improvements
on measures of dual-task standing balance and mobility, compared with an untrained
control group. [112] Their subjects were randomly assigned to a training group (n = 11) or
a no-treatment control group (n = 10). The results indicated that the training group
showed significant improvements in body sway during single-support balance and center
of gravity alignment during double-support dynamic balance whereas the control group
showed no appreciable improvements. They concluded that motor control in aging is
influenced by executive control, which has implications for theories of cognitive training
and transfer.
Kramer et al. (1995) examined whether the learning and performance of dual
tasks by young and old adults could be enhanced through training. [67] Adults were
trained with either a fixed-priority or variable-priority training strategy on a monitoring
task and an alphabet-arithmetic task and then transferred to a scheduling and a pairedassociates running memory task. Participants in the variable priority condition learned the
monitoring and alphabet-arithmetic tasks more quickly and achieved a higher level of
mastery on these tasks than did those in the fixed-priority condition. Moreover,
participants trained with the variable priority technique showed evidence of the
development of automatic processing and a more rapid rate of learning and higher level
of mastery of the transfer tasks than did the fixed-priority participants.
A recent study by Silsupadol et al. (2009) studied the performance comparison of
single vs. dual task training conditions in older adults with balance impairment.[113]
44
They compared the effect of 3 different approaches to balance training on dual-task
balance performance on 23 older adults with balance impairment (mean age, 74.8 years).
Participants were randomly assigned to 1 of 3 interventions: single-task training, dualtask training with fixed-priority instructions, and dual-task training with variable-priority
instructions. Participants received 45-minute individualized training sessions, 3 times a
week for 4 weeks. Gait speed under single-task and dual-task conditions was obtained at
baseline, the second week, the end of training, and the twelfth week after the end of
training. Their results showed that participants in all groups walked significantly faster
after training (p =.02). When a cognitive task was added, however, only participants who
received dual-task training with fixed-priority instructions and dual-task training with
variable-priority instructions exhibited significant improvements in gait speed (p<.001
and p<.001 respectively). In addition, only the dual-task training with variable-priority
instructions group demonstrated a dual-task training effect at the second week of training
and maintained the training effect at the 12-week follow-up. They concluded that dualtask training is effective in improving gait speed under dual-task conditions in elderly
participants with balance impairment. Training balance under single-task conditions may
not generalize to balance control during dual-task contexts. Explicit instruction regarding
attentional focus is an important factor contributing to the rate of learning and the
retention of the dual-task training effect.
Another pilot study by Plummer-D’Amato et al. (2012) found positive results
following dual task training on some tasks, when compared to single task training. [114]
The aim of the study was to compare single-task and dual-task training on obstacle
45
avoidance, gait speed and balance in healthy community-dwelling older adults. A total of
17 older adults (65-83 years) participated in a group circuit class, once weekly for 45 min
for 4 weeks. The dual-task group carried out cognitive activities simultaneously with gait
and balance exercises. The single-task training group carried out identical gait and
balance activities without cognitive tasks. They assessed time to complete a 6-m obstacle
course under single-task and three different dual-task conditions (spontaneous speech,
alphabet recitation and coin transfer), 25-ft gait speed, Timed Up and Go, and the
Activities-specific Balance Confidence Scale. Both groups showed significant
improvement in gait speed and Timed Up and Go. In addition, the proportion of
participants who achieved gait speed >1.0 m/s increased in both groups. There were no
within- or between-subjects differences in obstacle course performance under single or
dual-task conditions after the intervention. The authors concluded that once a week group
circuit training focusing on balance, gait and agility, with or without simultaneous
cognitive tasks, resulted in significantly improved walking speed among older adults.
However, training did not improve walking time or dual-task cost on an obstacle
negotiation task.
The recent surge of studies on dual task training demonstrates encouraging
outcomes on dual task performance. These results provide a new holistic dimension to
mobility research and subsequent rehabilitation.
2.4.5 Dual task assessment protocols
An equilibrated gait calls for motor planning in order to allow a feed-forward
strategy that adjusts the next step in an appropriate way. This means that gait requires
46
proactive dynamic postural control and orientation in space. An assessment of gait as an
aid to understanding balance control system in humans is therefore relevant in this
context.
There is evidence of a wide array of balance testing protocols, starting from
maintaining a still posture on a force platform, to normal walking, to obstacle crossing,
and testing motor aspects of balance using standardized tests like timed Get up and Go
(TUG) tests. In addition, there are many tests that involve concurrent cognitive tasks like
reciting letters of alphabets, mathematical calculations, auditory or visual Stroop test,
remembering a shopping list, counting to simply maintaining a conversation while
performing the motor task. The selection of a suitable method generally depends on the
goals and results aimed at. It is critical to choose the right task that would best suit the
target test population. For example, functional balance tests like Berg Balance Scale are
easy to perform and suitable for daily clinical use, but not always accurate enough.
Laboratory systems adept with the latest technologies provide detailed information about
the human balance control system of an individual.
In view of this vast evidence on the potential detrimental effects of dual-tasking,
and the commonness of dual tasking in daily living, several authors have suggested
examining a variety of different assessment measures. These include such things as
measurement of whole body center of mass (COM) motion, and its relative position to
the center of pressure (COP) of the supporting foot to examine gait stability, as well as
temporal characteristics of gait like cadence, stride length and double support time, COP
velocity and COP sway to test the motor aspect of a static or dynamic balance testing
47
protocol. [115] The cognitive aspect of the testing protocol typically includes assessing
verbal response time (VRT) and accuracy of verbal responses.
Many studies have investigated balance with static measurements, but most falls
occur during movement when the center of mass cannot be maintained within the lateral
borders of the base of support. Shumway-Cook et al. (2000) looked at TUG as their
motor task and counting backwards by 3’s as their cognitive task in their study on
community-dwelling older adults to identify fallers. [89] Lajoie and Gallagher (2004)
measured postural sway as a test for motor skills and simple reaction time (respond to
auditory stimulus) as a measurement of cognitive skills in their study. [116] Vaillant et al.
(2006) assessed motor skills using TUG and One Leg Balance test with left and right leg.
[117]
Cognitive task included subtraction by two’s, subtraction by fives or addition by
threes. Hyndman and Ashburn (2004) assessed their dual task performance using normal
walking with social interaction. [118] Their analysis was based on whether subjects
stopped walking for at least one second during conversation.
2.4.5.1 Timed Up and Go (TUG) test
The “Timed Up and Go test” (TUG) was presented in 1991 by Podsiadlo et al. as
a basic test for functional mobility. [119] This is an adaptation from the original Get up and
Go Test (GUG) by Mathias et al. (1986). [120] The test measures speed during several
functional maneuvers, which include standing up, walking, turning and sitting down. The
breakdown of phases are as follows: standing up from a seated position requires both
strength and technique, walking requires effective acceleration and deceleration abilities,
especially in preparation for a turn, turning is a challenging maneuver for older people
48
with balance disorders, and turning around to sit down challenges both balance and
orientation in adapting the body position to the chair.
Limited training and equipment are required, and the test is therefore convenient
in clinical settings. Good test-retest reliability (ICC = 0.97–0.99 and Spearmans = 0.93)
[89, 119, 121, 122]
have been demonstrated in many studies in community-dwelling, frail older
adults.
A study published by Shumway Cook et al. (2000) found TUG to be a sensitive
(sensitivity=87%) and specific (specificity=87%) measure for identifying elderly
individuals who are prone to falls. [89] They examined TUG under two conditions:
performance of TUG alone, and performance of TUG with addition of a simultaneous
secondary task. The results showed that performance of an additional task increased the
time taken to complete the TUG by 22% - 25%, independent of the type of secondary
task performed, with the greatest effect in the older adults with a history of falls.
2.4.5.2 Center of mass (COM) measure
To better reveal biomechanical mechanisms underlying age-related degeneration
in gait stability, and to enhance the assessment of falls risk, an accurate quantification of
a person’s balance maintenance during locomotion is needed. Several authors have
suggested assessment measures like whole body center of mass (COM) motion and its
relative position to the center of pressure (COP) of the supporting foot to examine gait
stability (temporal characteristics of gait like cadence, stride length and double support
time, COG velocity and COP sway to test the motor aspect of a static or dynamic balance
testing protocol. [115, 123]
49
The normal age-related changes in older adults affect joint kinetics in the body,
and may possibly influence COM motion in the mediolateral direction. Dynamic stability
during locomotion has been assessed using COM momentum, and an excessive lateral
momentum has been identified in balance-impaired elderly. [13, 124] Since human gait
includes a considerable single support period, control of mediolateral balance can be an
issue, particularly while transitioning support from one limb to the other. Excessive
frontal plane COM motion during gait may lead to loss of balance. [7, 14, 125-127]
Biomechanical measures of gait stability that can provide information on instantaneous
coordination between the COM and COP and exclude inter-subject variability, are still
needed.
COG velocity is another variable that is considered to a very useful tool in
identifying age related changes and fall risk. [128] [129, 130] Raymakers et.al (2005) reported
that mean COP displacement velocity provided a consistent measure differentiating
health conditions and age ranges, but interestingly, was not sensitive to the effects of a
secondary task. [130] The maximum horizontal separation distance between COM and
COP during single limb stance has been reported to sensitively quantify gait instability in
patients with bilateral vestibular hypofunction or cerebellar ataxia. [131] While this
measure appears to have great promise, it is subject to the biomechanical model that is
used to define the subject’s COM.
Recent studies on obstacle crossing [132, 133] demonstrated that linear measures of
COM motion in the frontal plane during obstacle crossing could better distinguish elderly
subjects with balance disorders from their age-matched healthy peers. However, do
50
younger healthy adults also respond to dual tasking in a similar way? This question still
needs to be addressed to interpret and understand the working of a typical balance
response mechanism in healthy adults when presented with a dual task situation. This
normative finding can serve as a reference tool for balance impaired, or older adults.
2.5 Conclusions
Collectively, the research presented in this review establishes the following: 1)
attention plays a significant role in gait and balance, 2) aging results in decrements in gait
and balance that may be particularly susceptible to the factors influencing attention, 3)
dual-tasking increases attentional demands and may have a greater effect in the elderly
than in younger populations, 4) training with dual-tasking may have a beneficial effect in
mitigating the adverse effects of dual-tasking, and 5) there are a variety of sensitive
techniques for assessing balance control both statically and dynamically. However, many
questions about the effects of dual-tasking on dynamic balance control still remain. In
particular, while the effects of dual tasking on gait in the elderly have been documented,
little is known about the kinematic and kinetic adaptations that correspond to these
effects. Hence, the objective of this study is to examine selected kinetic and kinematic
gait parameters in a population of well elderly during walking when attentional demands
are challenged both cognitively and motorically.
51
Chapter 3
Methodology
3.1 Participants
Eighteen healthy older adults were recruited for this research through assistance
from the university’s Center for Successful Aging, advertisement in local newspaper,
local senior centers and Area Office on Aging, in accordance with approved procedures
from the Biomedical Institutional Review Board at the University of Toledo. The
subjects’ participation was voluntary.
Inclusion criteria for this study specified that the subjects should be healthy older
adults aged between 65-90 years old who could perform a sit-to-stand activity, walk
without any aid or discomfort for a distance of about 3m, possess normal or corrected to
normal vision, and have no history of falls in the past six months. Participants with any of
the following medical conditions – history of falls, Alzheimer’s, Parkinson’s disease,
musculoskeletal or neurological disorder that affects gait, use of psychotropic medication,
major depression or other condition that could compromise balance or be exacerbated by
the testing procedure, or a score of ≤25 on the Mini Mental State Examination (MMSE)
[134]
were excluded from the study. The MMSE test is a screening tool that includes 11
52
questions in 6 sections, each representing a different cognitive domain or function
(orientation, registration, attention and calculation, recall, language, and copying). The
maximum score is 30. A score of 23 points or less has been considered to be evidence of
cognitive impairment, scores between 18 and 23 points indicate mild impairment, and
scores of 17 or less indicate severe impairment. [134] MMSE test was used to assess the
cognitive status of the participants and was administered by the investigator prior to the
testing session.
All subjects were provided with a complete description of the study prior to
testing, and those that agreed to participate were asked to sign an informed consent
document, approved by the UT IRB.
3.2 Testing Protocol
This project was conducted in the Motion Analysis Laboratory of the University
of Toledo. Prior to the laboratory testing session, participants underwent a phone
interview by the investigator to ensure that they meet the inclusion criteria with respect to
age and ability to independently walk a distance of 3m. Candidates who met the inclusion
criteria were invited to the laboratory for the actual testing. Testing was conducted over a
two hour session in a single day.
3.2.1 Administering the TUG and MMSE tests and collecting anthropometric data
Upon arrival at the laboratory, participants were orientated to the environment and
instruments to be used. Following the orientation, the informed consent procedure was
explained to them, and upon their approval for continuing the testing, they were asked to
53
sign the informed consent form. The researcher then completed an assessment of the
subject’s fall risk, presence of balance impairment, and cognitive status using the mean of
two trials of the timed Get up and Go (TUG) test and administering the MMSE test. The
time that participants took to complete the TUG test was noted by the investigator to be
used later during the session for collecting a baseline for cognitive task assessment. Age,
height and weight were recorded to create an accurate anatomical and biomechanical
model.
3.2.2 Data collection
Once the basic information was acquired, the actual testing session began. During
this, participants were required to perform approximately sixteen repetitions of the TUG
test, under four conditions. The TUG is a well-established functional test that involves
rising from a seated position in a chair, walking three meters, performing a 1800 turn,
returning to the chair and sitting down. Participants completed four repetitions of the
standard TUG test, and an additional twelve repetitions of the test with three dual tasking
modifications.
Trial Conditions were as follows 1. This involved the subject completing the standard TUG test with no
modifications.
2. The first modification of the TUG was the addition of a cognitive task, which
was counting backward by 3’s while completing the TUG test. This task was chosen to
simulate the dual tasking that we experience often in our lives, like rehearsing a to-do list
or a grocery list while maneuvering along the aisle in a grocery store.
54
3. The second modification of the test was the addition of a motor task, carrying a
simulated food tray while completing the TUG test. The simulated food tray (an apple, a
banana, peanut butter sandwich, 12 oz. plastic glass filled with water) was an effort to recreate a real life experience where one carries a dinner plate at home while walking.
4. And the third modification of the test involved the addition of both a cognitive
task and a motor task, simultaneously carrying the tray and counting backwards.
The sequence of these trials was randomized, and liberal rest (as needed) was
provided between the trials.
During all the TUG trials, audio-video data of the subject’s movement was
collected to provide a visual determination of the subject’s movement patterns and also to
record the audio for the trials involving the cognitive task for analysis. In addition, the
subject’s walking path during the TUG tests required them to pass over a force platform
that was used to record their walking ground reaction forces. This data, along with the
kinematic data was used to examine the effects of the conditions on joint kinetics and
balance control. A one second static trial was collected prior to the dynamic trials. This
static trial was a trial where the subject stood still with outstretched arms by the side, and
feet shoulder width apart. This is an obligatory step by the data collection software
Cortex 3.0 (Motion Analysis System Inc., Santa Rosa, CA) to establish the joint centers
for knee, ankle, and elbow joints. Following this, a dynamic trial was collected to assist
in the formation of a tracking template. This was a 7s trial in which the subject stood
stand still for the first two seconds, and then simulated marching in place, with reciprocal
arm swing so as to allow maximum joint movements at all the joints contributing during
55
the actual trial of TUG task. Following the creation of the template, the subject
underwent the 12 repetition trials of the TUG tests under the different modifications that
have been previously described.
Once the subject completed the TUG trials, a baseline measure for performance of
the cognitive task analysis was collected. This consisted of having the subject seated
comfortably on the chair while they counted backwards by 3’s for the set time (average
of two TUG trial times) from a randomly selected number between 30-100.
For assessment of the cognitive aspect of the task, starting numbers were
randomized. Also, participants were instructed to act on the word ‘GO’ for the pure
motor task of walking/ walking with the tray. For the cognitive trials of counting
backwards, the word ‘GO’ was replaced by the selected random number. A couple of
discrete practice sessions oriented the older adults to the cognitive and motor task, i.e.
cognitive task practice and a separate motor TUG test practice in an attempt to prevent a
learning effect during an actual task condition where these 2 tasks would be performed
simultaneously.
3.3 Instrumentation
3.3.1 Video data
A twelve camera Motion Capture system (Motion Analysis Inc., Santa Rosa CA)
was used to record video data of the participants movements during the trials, using
Cortex 3.6 (Motion Analysis Inc., Santa Rosa CA) software for collection and processing
of both the video and the force plate data, as seen in the figure 3-1 below.
56
Figure 3-1 The laboratory setup with cameras mounted on the wall and 3dimensional model of a subject, created in the Cortex 3.6 software
To facilitate motion capture, reflective markers (small foam balls) were attached
to specific anatomical landmarks with double sided tape. Video data was recorded at
100Hz. Captured data was processed in Cortex, smoothed with a fourth order low pass
Butterworth filter using a cut-off of 6 Hz. Following motion capture and processing the
data was transferred to Visual 3D software (C-Motion, Germantown, MD) for creation of
the biomechanical model and analysis. Using Visual 3D, a biomechanical model of the
participant was created, as seen in figure 3-2. This model was comprised of a thirteen
segments – bilateral foot, shank, thigh, forearm, and upper arm, in addition to pelvis,
thorax and head-neck, with six degrees of freedom for each segment.
57
Figure 3-2 Biomechanical model of a subject, showing reflective markers placed
on the specified anatomical locations, forming the 13 segments used to calculate the
whole body center of mass (COM) and the individual kinematic and kinetic variables, as
created in the Visual 3D software
58
3.3.2 Reflective marker placement
Reflective markers on bony surfaces allows reconstruction of the three
dimensional trajectory of surface markers, which is essential to data analysis. There were
two sets of marker used in this study – a static set and a dynamic set. The static markers
set augmented the dynamic markers set, and were placed at the medial -lateral and
proximal-distal ends of bones. This was necessary to accurately define the involved
anatomical segments and is an important step in building the 3D model. The static
markers were used for the static trial only, and were then removed during the actual
dynamic trials.
For the dynamic trial, beginning with the foot segment, markers were placed
bilaterally over the dorsum of the foot (over second and fifth metatarsal heads),
posterior aspect of the heel (at same level with foot marker) and lateral malleoli. To
create the shank segment, markers were placed bilaterally over lateral femoral condyle,
and three tracking markers were placed on the anterior and lateral aspect of the shank.
Thigh segment markers were placed over the greater trochanter bilaterally, with three
tracking markers placed in the mid-thigh region. Pelvis segment markers included
bilateral anterior superior iliac spine (ASIS), posterior superior iliac spine (PSIS), and a
marker over mid-point of the sacrum. The thorax segment included markers placed on
seventh cervical vertebra (C7), the right and left AC joints, the sternum and an offset
marker on the right scapula. For the upper arm segments, markers were placed bilaterally
over head of humerus, with three tracking markers over the mid-upper arm. Similarly,
the forearm segment markers consisted of bilateral lateral humeral epicondyle and
59
three tracking markers on the mid-forearm. The wrist was simply defined by markers
placed bilaterally on the radial styloid processes. The head–neck segment was defined
by markers placed bilaterally over the temple regions, antero-laterally (in order to
indicate the superior–inferior midpoint of the head–neck segment), and postero-laterally
head, in line with the temple, estimating the frontal plane of this segment.
Additionally, the static markers were placed on the medial femoral epicondyles
and medial humeral epicondyle, to estimate ankle, knee and elbow joint centers
respectively. The hip joint center positions were calculated using a directional estimate,
based on proportions of the distance between ASIS markers. The shoulder joint center
was calculated using a directional estimate, based on proportions of the distance between
the two shoulder markers and the head of humerus.
3.3.3 Force data
Ground reaction forces were recorded using two AMTI force plates (Model OR65-1, AMTI, Newton MA), sampled at a frequency of 1000 Hz. The force plates
simultaneously measured three force components along the XYZ axes and three moment
components about the XYZ axes. The forces and moments were measured by strain
gauges attached to proprietary load cells near the four corners of the platform. GRF data
was recorded through the Cortex software and smoothed using a low pass fourth order
Butterworth filter at 10 Hz. Data collected from the video system and force platforms
were time synchronized in Cortex. Additionally, the video calibration process that is part
of Cortex established the origin of the lab coordinate system and linked the origin of the
force platforms to that of the video system.
60
3.3.4 Cognitive task
Cognitive test performance was assessed by evaluating each participant’s
response time (RT) and response accuracy (RA) in reciting the numbers. In order to
analyze the cognitive data, a time synchronized video camera recorded the video and
audio aspect of the testing protocol for each participant. The variables of interest, i.e.
verbal response time (RT) and verbal accuracy (RA) were calculated manually by the
investigator at a later time. RA was calculated by counting the number of correct verbal
responses during completion of the TUG and expressing this as the ratio of the number of
correct responses over the amount of time required to complete the TUG test.
3.4 Statistical Analysis
3.4.1 Dependent Variables
The dependent variables of interest for this study can be classified into gait,
biomechanical and cognitive categories.
Temporal-spatial variables included: duration of the task, gait speed, stride
length, step width, cadence, double support time, and swing time.
Kinetic-kinematic variables included: peak ground reaction force in frontal
plane, total center of mass displacement in the frontal plane and mean center of mass
velocity in the frontal plane during a single gait cycle.
Cognitive variables included: verbal RT (response time) and verbal RA
(response accuracy). Verbal response time (RT) was measured by dividing the number of
61
responses over the total time to complete the task. Verbal accuracy (RA) was measured
by dividing the number of accurate responses over number of total response.
3.4.2 Independent variables
The independent variable was ‘task condition’. There were four conditions for the
testing protocol.
W – TUG test alone
WT – TUG + carrying the tray
WC – TUG + counting backwards by 3’s
WCT – TUG + carrying the tray and counting backwards by 3’s.
3.4.3 Statistical Tests
Multiple one-way repeated measures ANOVAs were performed for each of the
temporal-spatial and kinetic-kinematic variables to determine the differential effects of
the conditions on the dependent variables. A priori alpha level was set at p < 0.008 level
for gait variables (to reduce type 2 error, p = 0.05/7 = 0.008).
Paired sample t test were performed for the cognitive trials to find if there were
differences in response accuracy and timing among older adults. A priori alpha was set at
p < 0.05. All statistical tests were executed using SPSS 21 software (IBM SPSS Statistics
21, USA).
62
Chapter 4
Results
4.1 Subject Description
A total of 18 healthy older adults were evaluated for potential recruitment. Of
these, two people did not meet the inclusion criteria (1 had difficulty getting up from a
seated position, the other had bilateral knee replacement and exhibited slow cautious gait
as well as an unsafe technique of getting up from a seated chair) and a third subject
completed the testing procedure, but could not be analyzed due to technical problems.
The baseline demographic data of the subjects who met all of the inclusion criteria and
completed testing are presented in table 4.1. Table 4.2 presents the clinical characteristics
of the tested population; their scores on the Mini Mental State Examination (MMSE) test,
time for performing the TUG test and cognitive accuracy and timing.
63
Table 4.1 Mean (± S.D) age, height and weight of older adults
Age (years) ± SD
74.64 ± 8.18
Gender
Height (m) ± SD
Weight (kg) ± SD
1.66 ± 0.11
73.42 ± 8.33
11 females
4 males
Table 4.2 Summary of the baseline clinical characteristics (MMSE test scores,
TUG timing, cognitive accuracy and cognitive response timing with their SD)
MMSE ± SD
(out of 30)
29.29 ± 0.9
TUG ± SD (s)
Cognitive
Cognitive response
accuracy (%)
timing ± SD (s)
100
0.62 ± 0.15
7.7 ± 1.53
(MMSE: Mini Mental State Examination Scores, TUG: Timed Get Up and Go test)
4.2 Temporal-spatial distance parameters
The temporal/distance gait values for all the tasks (W, WC, WT, and WCT) are
presented in table 4.2.1. Some temporal-spatial parameters like gait speed, stride length,
step width were normalized to the leg length of each subject, calculated as the distance
from right hip joint center to the right heel. Thus, velocity was expressed as m/sN
(normalized meters per second) and displacement was expressed as mN (normalized
meters). This was done to help account for the between subject anthropometric
64
variability that is characteristic of a diverse population of elderly subjects. The method
of normalization that we chose is one of several that have been used in gait research for
many years. [14] Duration of the task represents the time taken to complete a trial.
Additionally, based on the ANOVA that was performed for each parameter, the
statistical significance of the between condition comparisons is listed.
65
Table 4.3 Temporal-spatial parameters (Mean ± SD) are represented with their
statistical significance values across all task conditions
W ± SD
WC ± SD
WT ± SD
WCT ± SD
P values
7.57 ± 1.61
8.66 ± 1.69
8.61 ± 1.64
9.40 ± 1.68
< 0.001 ⃰
1.77 ± 0.36
1.59 ± 0.31
1.61 ± 0.32
1.55 ± 0.36
0.002 ⃰
Cadence
161.15 ±
161.22 ±
159.58 ±
153.76 ±
(steps/min)
29.5
38.42
31.67
28.61
1.60 ± 0.21
1.50 ± 0.22
1.47 ± 0.24
1.49 ± 0.23
0.001 ⃰
0.16 ± 0.05
0.16 ± 0.07
0.16 ± 0.06
0.15 ± 0.06
0.946
0.17 ± 0.05
0.24 ± 0.08
0.21 ± 0.05
0.24 ± 0.06
< 0.001 ⃰
0.38 ± 0.04
0.37 ± 0.04
0.37 ±0.07
0.37 ± 0.06
0.940
Duration of
task (s)
Gait speed
(m/sN)
Stride length
(mN)
Step width
(mN)
0.497
Double
support time
(s)
Swing time
(s)
W: TUG, WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a meal tray, WCT: TUG
+ Counting backward by 3’s + Carrying a meal tray. ⃰ Significant differences at P <0.008 (adjusted p value,
p = 0.05/7)
66
4.2.1 Duration of task
This variable was calculated as the time taken by the participants to complete the
TUG task. Upon statistical analysis, it was found that Mauchly’s test of sphericity was
violated (χ2 (5) = 15.10, p =0.010), and therefore a corrected value (Greenhouse-Geisser
correction) of F (1.84, 25.86) = 48.51 was used. Results of the one way repeated
measures ANOVA revealed that the subjects differed significantly across task conditions,
F (1.84, 25.86) = 48.51, p < 0.001 (interpreted at adjusted p value of p < 0.008). Post hoc
analysis with Bonferroni correction showed that subjects walked significantly faster in
the W condition (7.57 ± 1.61, p < 0.001) compared to other conditions of WC, WT and
WCT (8.66 ± 1.69, p < 0.001, 8.61 ± 1.64, p < 0.001 and 9.40 ± 1.68, p < 0.001
respectively). Duration of WCT trials was longer than WT (p < 0.001). There were no
significant differences between the WC (secondary cognitive task) and the WT
(secondary motor task) conditions (p = 0.48). The effect size for this variable was large
(0.8).
The graph for duration of the task is presented in figure 4-1.
67
12.00
10.00
9.40
8.66
Duration of task (s)
8.00
8.61
7.57
6.00
4.00
2.00
0.00
W
WC
WT
Task Condition
WCT
Figure 4-1 Graph comparing duration of task (± SD) across all task conditions. W:
TUG, WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a meal tray, WCT: TUG + Counting
backward by 3’s + Carrying a meal tray
4.2.2 Gait speed
The overall results of the one way repeated measures ANOVA demonstrated that
the group means across the task conditions were significantly different for gait speed, F
(3, 35.5) = 5.698, p = 0.002 (adjusted p value of <0.008) in healthy older adults. Post hoc
analysis with Bonferroni correction revealed that older adults walked slower in their WC
task (1.59 ± 0.31 m/sN) compared to W trials (1.77 ± 0.36 m/sN), but this was not
statistically different (p = 0.129). However, the task condition of WT (1.61 ± 0.32 m/sN)
and W were statistically different (p = 0.044), as were the differences exhibited (p =
68
0.012) between the WCT (1.55 ± 0.36 m/sN) and W task conditions. Figure 4-2 illustrates
these results. Gait speed showed a small-moderate effect size at 0.3.
2.50
Gait speed (m/sN)
2.00
1.77
1.50
1.61
1.59
1.55
1.00
0.50
0.00
W
WC
WT
Task Condition
WCT
Figure 4-2 Graph comparing gait speed (± SD) across all task conditions in
healthy older adults. W: TUG, WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a meal
tray, WCT: TUG + Counting backward by 3’s + Carrying a meal tray
4.2.3 Cadence
Cadence was calculated as number of steps/min. The results of cadence show that
the Mauchly’s test of sphericity was violated (χ2 (5) = 16.673, p < 0.05), and therefore a
corrected value (Greenhouse-Geisser correction) of F (1.65, 23.18) = 0.663 was used.
69
The overall results of the one way repeated measures ANOVA showed that the
subject’s cadence did not differ statistically across the different tasks F (1.65, 23.18) =
0.663, p = 0.497. The effect size for cadence was very small, at 0.04.
The graph showing task comparisons is expressed below in figure 4-3.
250.00
Cadence (# steps/min)
200.00
150.00
161.15
161.22
159.58
W
WC
WT
153.76
100.00
50.00
0.00
Task Condition
WCT
Figure 4-3 Graph comparing cadence (± SD) across all task conditions. W: TUG,
WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a meal tray, WCT: TUG + Counting
backward by 3’s + Carrying a meal tray
4.2.4 Stride length
Stride length was calculated as the distance between the proximal end position of
the foot at ipsilateral heel strike to the proximal end position of the foot at the next
ipsilateral heel strike in a gait cycle.
70
A one way repeated measures ANOVA, with adjusted p value of p <0.008,
showed that the group means across task conditions differed significantly for stride length
F (3, 42) = 6.777, p = 0.001. The subjects walked with a stride length of 1.6 ± 0.21mN in
the W task condition, 1.5 ± 0.22 mN in WC condition, 1.47 ±0.24 mN in the WT
condition and 1.49 ± 0.23 mN in the WCT condition. Stride length had a small-moderate
effect size of 0.31. Upon conducting a post hoc analysis, it was revealed that subjects
walked with shorter strides in the both the WT and WCT conditions (p = 0.012 and p =
0.014 respectively), when compared to W condition. Figure 4-4 illustrates these results.
2.00
1.80
Stride length (mN)
1.60
1.40
1.60
1.50
1.47
1.49
WC
WT
WCT
1.20
1.00
0.80
0.60
0.40
0.20
0.00
W
Task Condition
Figure 4-4 Graph comparing stride length (± SD) across all tasks. W: TUG, WC:
TUG + Counting backward by 3’s, WT: TUG + Carrying a meal tray, WCT: TUG + Counting backward by
3’s + Carrying a meal tray
71
4.2.5 Step width
Step width was calculated as the medio-lateral perpendicular distance between
proximal end position of the foot at ipsilateral heel strike to the proximal end position of
the foot at the contralateral heel strike.
One way repeated measures ANOVA showed that the group means across task
conditions were not significantly different for step width F (3, 42) = 0.123, p = 0.946. It
was surprising to see that the means did not differ across conditions W, WC and WT.
And that, although not statistically different, actually decreased slightly during the WCT
trials The W task condition had a step width of 0.13 ± 0.05 mN, 0.16 ± 0.07 mN was the
value in the WC condition, 0.16 ±0.06 mN in the WT condition and 0.16 ± 0.0.06 mN in
the WCT condition. Step width exhibited a very small effect size of 0.009. Figure 4-5
illustrates these results.
72
0.25
Step width (mN)
0.20
0.15
0.16
0.16
0.16
W
WC
WT
0.15
0.10
0.05
0.00
Task Condition
WCT
Figure 4-5 Graph comparing step width (± SD) across all task conditions in
healthy older adults. W: TUG, WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a meal
tray, WCT: TUG + Counting backward by 3’s + Carrying a meal tray
4.2.6 Double support time
The human gait cycle has a double support phase in which both feet are in contact
with the ground simultaneously and the body weight is supported by both legs.
When conducting the ANOVA for this variable, Mauchly’s test of sphericity was
violated, hence a corrected Greenhouse-Geisser correction value was applied. The one
way repeated measure ANOVA showed that the group means across the task conditions
were significantly different. F (1.71, 24.04) = 15.288, p <0.001 (adjusted p value of p<
0.008). The subjects evidenced a double support time of 0.17 ± 0.05 s in the W task
73
condition, 0.24 ± 0.08 s in WC condition, 0.21 ± 0.05 s in the WT condition and 0.24 ±
0.06 s in the WCT condition. Post hoc pairwise comparisons with a Bonferroni correction
revealed that stride length was significantly lower across the task conditions W and WC
(p = 0.001), W and WT (p = 0.000), W and WCT (p = 0.000), but not between the types
of secondary tasks WC and WT (p = 0.54) and also between WC and WCT (p = 1.0).
This variable had a moderate effect size of 0.5 on task conditions.
The graph showing task comparisons for double support time in older adults is
represented below, figure 4-6.
0.35
0.30
Double Support time (s)
0.25
0.24
0.24
0.20
0.15
0.21
0.17
0.10
0.05
0.00
W
WC
WT
WCT
Task Condition
Figure 4-6 Graph comparing double support time (± SD) across all task conditions
in healthy older adults. W: TUG, WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a
meal tray, WCT: TUG + Counting backward by 3’s + Carrying a meal tray
74
4.2.7 Swing time
The amount of time a person spent in the swing phase was termed as ‘swing
time’. Swing time values for both the right and left side were averaged from a single gait
cycle. The results for the swing time variable show that the Mauchly’s test of sphericity
was not violated.
Results of the one way repeated measures ANOVA showed that the group means
across task conditions were not significantly different for swing time F (3, 42) = 0.155, p
= 0.926. In the W condition, subjects spent an average of 0.38 ± 0.04 s in the swing
phase, 0.37 ± 0.04 s in WC condition, 0.37 ±0.07 s in the WT condition and 0.37 ± 0.06 s
in the WCT condition. Swing time had a very minimal effect size of 0.009.
The figure 4-7 below shows the task comparisons of swing time.
75
0.50
0.45
0.40
Swing time (s)
0.35
0.38
0.37
0.37
0.37
W
WC
WT
WCT
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Task Condition
Figure 4-7 Graph comparing swing time (± SD) across all task conditions in
healthy older adults. W: TUG, WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a meal
tray, WCT: TUG + Counting backward by 3’s + Carrying a meal tray
4.3 Kinetic-Kinematic parameters
In addition to evaluating the temporal/distance gait parameters, we also examined
selected biomechanical variables to provide specific insight into the effects of the
conditions on control parameters related to dynamic balance. These variables are
expressed in the table 4.3 below. Center of mass (COM) distance and velocity are
normalized to the leg length (right hip joint to right lateral malleolus of ankle) while
ground reaction force (GRF) is normalized to body weight.
76
Table 4.4 Means and SD of selected kinetic-kinematic parameters across task
conditions in healthy older adults
W ± SD
WC ± SD
WT ± SD
WCT ± SD
P value
0.22 ± 0.07
0.21 ± 0.11
0.24 ± 0.10
0.23 ± 0.05
0.862
0.09 ± 0.02
0.10 ± 0.04
0.11 ± 0.02
0.08 ± 0.02
0.011⃰
0.31 ± 0.10
0.32 ± 0.14
0.35 ± 0.13
0.41 ± 0.11
0.104
Total COM
excursion in
frontal plane
(mN)
Peak GRF in
frontal plane
(BW)
Mean COM
excursion
velocity in
frontal plane
(m/sN)
W: TUG, WC: TUG + Counting backward by 3’s, WT: TUG + Carrying a meal tray, WCT: TUG
+ Counting backward by 3’s + Carrying a meal tray, ⃰ Significant differences at P <0.016 (adjusted p value)
4.3.1 Total COM excursion in the frontal plane
This variable was calculated as the difference between the maximum and
minimum distance that the whole body COM travelled in the frontal plane in a single gait
cycle.
77
Upon statistical analysis, it was found that Mauchly’s test of sphericity was not
violated in this case. One way repeated measures ANOVA revealed that total COM
excursion in the frontal plane did not show significant differences across the task
conditions. F (3, 42) = 0.248, p = 0.862 and a very low effect size of 0.017.
The graph for this parameter is presented below in figure 4-8.
Total COM excursion in frontal plane mN)
0.40
0.35
0.30
0.25
0.20
0.24
0.22
0.21
W
WC
0.23
0.15
0.10
0.05
0.00
WT
WCT
Task Condition
Figure 4-8 Graph comparing total COM distance in frontal plane (± SD) across all
task conditions in healthy older adults. W: TUG, WC: TUG + Counting backward by 3’s, WT:
TUG + Carrying a meal tray, WCT: TUG + Counting backward by 3’s + Carrying a meal tray
4.3.2 Peak GRF in the frontal plane
The maximum ground reaction force (GRF) exerted by a subject in the frontal
plane in the single stance phase of the gait cycle was calculated, and termed as Peak GRF
78
in the frontal plane. Typically, the maximum force occurred immediately following heel
strike and was medially directed. Upon statistical analysis, one way repeated measures
ANOVA revealed that the subjects differed significantly for this variable across task
conditions, F (3, 42) = 4.18, p = 0.011 with a small effect size of 0.23. Post hoc pairwise
comparisons with Bonferroni correction revealed that differences existed between the W
and WC (p =.009), W and WCT (p = .027) and WC and WT (p = .030) task conditions.
Figure 4-9 graphs the differences across task conditions with respect to this
variable.
0.14
Peak GRF in frontal plane (BW)
0.12
0.11
0.10
0.08
0.09
0.10
0.08
0.06
0.04
0.02
0.00
W
WC
WT
WCT
Task Condition
Figure 4-9 Graph comparing peak GRF values (± SD) in older adults across all
task conditions. W: TUG, WC:TUG + Counting backward by 3’s, WT: TUG + Carrying a meal tray,
WCT: TUG + Counting backward by 3’s + Carrying a meal tray
79
4.3.3 Mean COM velocity in frontal plane
COM velocity in the medio-lateral direction is a frequently used parameter for
testing dynamic balance. This variable was calculated as the integral of the displacement
of the modeled COM position within the frontal plane, over a single gait cycle time
Integral between two frames = (ValueAtFrame1 + ValueAtFrame2) / (2*Delta T),
where T= time between frames
Mean COM velocity = Sum of the integrals for all frames in a gait cycle
Statistical analysis with one way repeated measures ANOVA revealed that, while
the data revealed the anticipated trend, the subjects did not differ significantly in terms of
mean COM velocity in the frontal plane across the four task conditions, F (3, 42) = 2.188,
p = 0.104. The W condition had a mean ± SD of 0.31 ± 0.10 m/sN, WC of 0.32 ± 0.14
m/sN, WT of 0.35 ± 0.13 m/sN and WCT had a mean ± SD of 0.41 ± 0.1 m/sN. This
variable showed a small effect size of 0.14.
Figure 4-10 graphs the differences across task conditions with respect to the
subject’s mean COM velocity in the frontal plane during a single gait cycle.
80
Mean COM Velocity in frontal plane
(m/sN)
0.60
0.50
0.40
0.30
0.41
0.35
0.31
0.32
W
WC
0.20
0.10
0.00
WT
WCT
Task Condition
Figure 4-10 Graph comparing peak mean COM velocity in frontal plane (± SD)
across all task conditions W: TUG, WC:TUG + Counting backward by 3’s, WT: TUG + Carrying a
meal tray, WCT: TUG + Counting backward by 3’s + Carrying a meal tray
4.4 Cognitive parameters
As a part of examining the effect of a secondary cognitive task on TUG, counting
backward by 3’s was the chosen task. Trials with counting activities, WC and WCT were
analyzed to yield 2 variables: response accuracy (calculated as #correct response / total
#of response) and response timing (calculated as total number of responses / time taken to
complete the trial).The values ± SD are represented in table 4.4.
81
Table 4.5 Mean ± SD of cognitive parameters across cognitive trials in older
adults
Task condition
Response accuracy (%) ± SD
Response timing (s) ± SD
WC
87.34 ±16.73
0.56 ± 0.13
WCT
87.96 ± 15.48
0.53 ± 0.13
WC:TUG + Counting backward by 3’s, WCT: TUG + Counting backward by 3’s + Carrying a meal tray
4.4.1 Response accuracy
A paired-samples t-test was conducted to compare accuracy of response in WC
and WCT conditions. There was no significant difference in the scores for WC (M =
87.34, SD = 16.73) and WCT (M = 87.96, SD = 15.48) conditions; t (14) = -.131, p =
0.897. These results suggest that the addition of carrying a tray while counting backwards
during the task did not change the subject’s accuracy in completing the counting task.
4.4.1 Response timing
A paired-samples t-test was conducted to compare the number of responses per
second in WC and WCT conditions. There was no significant difference in the scores for
WC (M = 0.56, SD = 0.13) and WCT (M = 0.53, SD = 0.13) conditions; t (14) = .568, p =
0.579. These results suggest that there were no differences in the frequency of counting
when subjects performed the task while carrying the tray versus not carrying the tray.
82
Chapter 5
Discussion
1.
It was hypothesized that temporal gait characteristics including cadence, step
length and stride length will be decreased with increasing task complexity. Double
support time, and step width will increase with increasing task complexity.
2.
It was hypothesized that center of mass displacement in the frontal plane will
increase with increases in task complexity.
3.
It was hypothesized that center of mass velocity in the frontal plane will increase
with increases in task complexity.
4.
It was hypothesized that verbal response time and accuracy will decrease with
increasing task complexity.
5.1 Introduction
The purpose of this study was to investigate the effects of dual tasking on
measures of gait and dynamic balance in a population of healthy elderly individuals.
This was done through assessment of frontal plane COP displacement and velocity,
temporal-distance gait characteristics, verbal response time and accuracy during a timed
Get-Up-And-Go test (TUG) test with a concurrent cognitive task of counting backward
83
and a concurrent motor task of carrying a meal tray. Collectively, the results of the study
show that selected gait and dynamic balance indices were adversely affected by the
subject’s attempts to accommodate for the difficulty imposed by the varied level of the
task conditions.
5.2 Evidence in support of hypothesis 1: It was hypothesized that selected temporalspatial gait characteristics, including cadence, step length and stride length would
be decreased with increasing task complexity. And, double support time, and step
width would both increase with increasing task complexity.
As described in Chapter 3, task complexity progressed in increasing order as
follows:
W= TUG (Timed Up and Go test)
WC = TUG + Counting backward by3’s
WT = TUG+ Carrying a meal tray
WCT = TUG +Counting backward by 3’s + Carrying a meal tray
5.2.1 Temporal-spatial parameters
The ability to control balance while walking is a fundamental skill that is
frequently compromised by advanced age. And under circumstances of dual tasking
where divided attention plays a significant role in affecting the primary motor task (gait,
in this study), changes in temporal-spatial characteristics are highly expected. This is
reflected in hypothesis 1.
A factor that relates to all of the gait characteristics indicated in hypothesis 1 is
the degree to which the gait of the subjects in this study was comparable to that of typical
84
elderly individuals. If the gait of our subjects was highly abnormal, then it is likely that
their response to the different task conditions would follow predicable trends. Speed of
gait has often been used as a general measure of gait normality. Thus, we examined the
subject’s gait to determine if the subjects in this study were representative of a normal
elderly population.
In this study, we found that older adults walked with slower speed during dual
task conditions of WC (1.59 ± 0.31 m/sN, which is -10.16% slowing), WT (1.61 ± 0.32
m/sN, which is -9.03% slowing) and WCT (1.55 ± 0.36 m/sN, which is -12.42% slowing)
when compared to single task of W (1.77 ± 0.36 m/sN).
A number of studies have examined walking speeds in older adults under a
variety of conditions. Although difficult to make a direct comparison to other studies
because of variation in method of normalization, our results are somewhat consistent with
the other studies of gait in the elderly population. Van Iersel et al. (2007) [97] examined
the effect on balance of three different cognitive dual tasks performed while walking and
found the subject’s average normalized gait speed to be between 1.63 ± 0.24 m/s and 2.0
± 0.19 m/s under fast and very fast walking conditions respectively. Ko et al. (2010) [12]
investigated the effects of walking under different challenges on the gait kinematics and
kinetics generated during these activities and how these vary with age. They found an
average gait speed of 1.66 m/s under fast walking conditions in healthy older individuals,
after they adjusted for height in their regression model analysis.
85
Kerrigan et al. (1998) [7] and Hernandez et al. (2005) [8] found an average walking
speed of 1.55 ± 0.2 m/s and 1.55 ± 0.15 m/s respectively, which is a slightly lower than
what we found (1.77 ± 0.36 m/sN). However, their study did not include normalization.
In this study the subjects showed significant differences across the task conditions
in their gait speed, stride length, double support time, as well as in the time taken to
complete the task. Their cadence, step length and time spent in the swing phase of the
gait cycle, however, did not change with increasing task complexity, as had been
hypothesized.
Our result of decreasing gait speed with dual tasking is consistent with other
studies that have seen decrements in gait performance when a person is challenged with a
concurrent secondary task. ([97, 114, 138]
While subjects appeared to walk slower under all three task conditions than when
not being influenced by a secondary task, the difference for the WC condition was not
significant. Given that the comparisons for both of the other two conditions were
significant, it is clear that the presence of a secondary task (in this case either carrying a
tray or carrying a tray and simultaneously counting) does influence walking. The fact that
the subjects slowed their gait speed under these conditions suggests that the involved
dual-tasking created demands on the CNS that necessitated a slower and more deliberate
gait pattern. Further, it appears that a secondary motor task had a greater effect on gait
speed than did a secondary cognitive task.
Gait speed is a function of two factors, stride length and cadence. The results of
previous research on stride length in elderly individuals are similar to what was seen in
86
this study. Shkurotova et al. (2004)
[100]
found stride length to be 1.5 m in their study in
the fast walking condition, while van Iersel et al. (2007) [97] found stride length to be
between 1.55 ± 0.17 m and 1.65 ± 0.19m under fast walk and very fast walking condition
in older adults.
Looking at our stride length results across the various task conditions, we can
see that the subjects appeared to shorten their strides under all three of the dual task
conditions, which is consistent with the slower walking speeds discussed above. A mean
stride length of 1.6 ± 0.21 mN was found in the single task (W), and was significantly
decreased in the dual task conditions of WCT (1.49 ± 0.23 mN, which is -6.8% less than
the W condition) and WT (1.47 ± 0.24 mN, which is -8.1% less). However, the WC task
condition (1.5 ± 0.22 mN) was not significantly different from the W condition, though
that equals to -6.2% decrease in stride length compared to the W condition. Thus, it
appears that the subjects responded to the presence of a secondary dual task by shortening
their stride. However, this occurred to a lesser degree when the secondary task was a
cognitive task (i.e. counting) than when the secondary task was a motor task (i.e. carrying
a tray). As normal walking is primarily a motor task, this may reflect the fact that an
increase in the demands placed on those CNS structures responsible for carrying out
motor tasks has a greater impact on motor function than when additional cognitive
demands are added.
Interestingly, there were no significant differences across the four conditions in
the subject’s cadence. Previous research has established that, typically, individuals make
subtle changes in walking velocity by manipulating stride length while holding cadence
87
constant. As walking velocity is further increased, stride length becomes constant and
cadence is increased. The reverse pattern is followed when velocity is decreased.
Although the data in this study shows a trend of decreasing cadence as task complexity
increases (Table 4.3, Fig. 4-3) the differences between the tasks were not significant.
Older adults did not show statistical differences in cadence across task conditions (p =
.497). Addition of a secondary concurrent, task, motor or cognitive, did not influence the
cadence values in older adults, over the single task condition of just walking. The mean
values of cadence for the WCT task condition was the lowest (153.76 ± 28.61), and those
of W, WC and WT condition were (161.15 ± 29.5, 161.22 ± 38.42, 159.58 ± 31.67)
respectively. Our cadence values are slightly higher than those of Ko et al. (2010) [12] who
found a mean value of 140.20 for their fast walking speed in elderly individuals, and
Shkuratova et al. (2004) [100] whose cadence at fast walking speed in an elderly
population, was 134.37 ± 13.56. Again, the fact that our subjects had slightly higher
cadence values than those previously reported could be a function of the inclusion criteria
we imposed.
Why cadence did not decrease more across the three dual-tasking conditions is
somewhat puzzling. Perhaps this was due to the fact that the decreases in velocity that
were necessitated by dual-tasking could be largely accommodated by changes in stride
length alone, without the necessity of substantial decreases in cadence. And, that the
elderly subjects found it more comfortable to shorten their stride than to alter the rhythm
of their gait.
88
Double support time increased significantly when a secondary simultaneous
task was added to the primary TUG condition. It was statistically different between the
W and WC conditions (p < 0.001) and represented an increase in double support time of
about 41%, for the WT comparison (p < 0.000) there was an increase of about 23.5%, and
for the WCT comparison (p < 0.000) there was an increase of about 41%.
Double support time is believed to primarily represent balance control. [139]
Many previous studies have established the fact that, as stability during gait diminishes,
individuals attempt to compensate by increasing double support time. This is due the
enhanced ability to generate stabilizing moments when the base of support is enlarged by
having both feet on the ground simultaneously. The fact that all three dual-tasking
conditions appeared to necessitate an increase in double support time, indicates that, to
some degree, all three interfered with the control of balance. Thus, when presented with a
situation where the CNS was occupied by more than simply the task of walking, subjects
became increasingly unstable and attempted to compensate by elongating the double
support phase of the gait cycle. This phenomenon is evident by the graphs of the two
secondary tasks in figure 4-6. Upon careful observation, we can see how our subjects
spent more time in their double support phase in the cognitive task (0.24s), compared to
motor task (0.21s), and this increased time is interpreted as an exhibition of instability,
rather than safety.
Duration of the task, (i.e. time taken to complete each trial) can provide an
additional dimension to the previous discussion of gait speed. Gait speed was calculated
over 1 gait cycle, while timing of the task took into account the time spent in getting up
89
from a chair, walking, turning around, walking back and sitting down on the chair (i.e.
the entire TUG test). Older adults were the fastest to complete the task under the W
condition (7.56 ± 1.6 s) and took the longest time under the WCT condition (9.4 ±1.6s)
accounting for 24% increase. This was an expected outcome and compliments the results
found for gait speed, where the subjects had the highest gait speed in the W condition,
and the slowest in the WCT condition. Again, the results demonstrate that adding a
secondary task increased the time that it took the subjects to finish a task. However, the
type of secondary task (motor or cognitive) did not have a statistically significant
influence on the outcome. Again, this is similar to what we found for gait speed (WT and
WC had similar walking speeds). Our results are similar to those of Shumway-Cook et al.
(2000) [89] who found subject’s times on the TUG to be 8.4 ±1.7 s, TUG and motor task
to be 9.7 ±1.6 s and TUG with a cognitive task to be 9.7 ± 2.3 s, emphasizing that there
were no difference with regard to the type of secondary concurrent task (motor or
cognitive) when it came to the time a person took to complete the task. A related
observation of interest is the fact that the percent difference between the W condition and
the other three conditions for our subjects was relatively the same for the double support
variable and the duration of task variable. The double support variable represents a
portion of a single gait cycle. In contrast, the duration of task variable reflects far more,
in terms of movement complexity, as it incorporates all aspects of the TUG test (standing,
walking, turning, sitting). The similarities in how these two variables were affected by the
conditions may be an indication that all of the different components of the TUG are
equally affected by the presence of secondary cognitive and motor tasks.
90
As with the variable double support, previous studies have demonstrated that,
when balance is compromised, individuals tend to increase their step width. This is done
to enlarge the base of support during double support, which improves the ability to
generate a medial/lateral stabilizing. Surprisingly, in our study the subjects maintained
their step width across the four different task conditions (W = 0.16 ± 0.05 mN, WC =
0.16 ± 0.07 mN, WT = 0.16 ± 0.06 mN, WCT = 0.15 ± 0.06 mN). Ko et al. (2010) [12]
found similar values for stride width (which is double the value of step width) of 0.1 m in
fast walking in an elderly population. As mentioned earlier, slight increase in our values
could possibly results from normalizing our data. However, step width did not differ in
older adults across task conditions (p = .946). So, why did our subjects maintain a
constant step width, when other variables clearly demonstrate that the secondary tasks
were adversely affecting balance? It is our belief that this was the result of the testing
protocol, rather than a reflection of any unique gait adaptations. The TUG test requires
the subject to walk a short distance towards a specific location (marked by a cone),
execute a 180 degree turn, and return to the starting location (chair). Subjects are
instructed to complete the test as fast as possible. Subjects may intuitively comprehend
that the fastest way to complete the test is to follow a straight line to and from the cone.
And, that any lateral deviation (i.e. increase in step width) in their gait pattern will move
them away from the target location and may slow them down. Thus, the subject’s goal of
completing the test as fast as possible may have over-ridden the natural inclination to
increase step width as the task became increasingly balance compromised.
91
Swing time was defined as the duration of the period of time when the foot was in
the air during single support on the opposite side. It was calculated as the time taken from
toe off to heel strike of the same foot, over a single gait cycle. Swing time was selected as
a variable to be examined because of its complementary relationship to step width.
Typically, as step width increases, swing time increases. This is due to the fact that the
swing of the free leg is what positions the foot for the start of the next double supports
period, during which the measurement of step with is made. It follows logically that when
step width is increased to improve stability, greater time (swing time) would be required
to move the foot into position for heel strike. As previously discussed, our subjects did
not differ across conditions in their step width. Thus, it is not surprising that swing time
did not yield statistically significant results (p = 0.940). The subjects spent an average of
0.45 ± 0.03 s in the swing phase, in the W condition, and this did not change over other
task conditions (WC = 0.45 ± 0.05 s, WT = 0.45 ±0.07 s, WCT = 0.44 ± 0.06 s).
5.2.2 Conclusion
As an overall conclusion, task complexity had an adverse effect on gait speed,
stride length, double support time and the overall time taken to complete the task in our
subject population, which is consistent with findings of other studies that tested older
adults under some form of task difficulty like obstacle crossing, or figure of eight
walking, or post fatigue. [9, 12, 100, 114, 137] Our subjects decreased their gait speed, stride
length and increased their double support time when a secondary task was introduced to
the primary condition of a TUG. Reduced walking speed and stride length and increased
double-limb support duration in older adults are usually interpreted as age-related
92
adaptations to produce gait that is safer and less destabilizing, to compensate for
increased accelerations acting on the body during fast walking. Thus, Hypothesis 1 is
partially supported.
5.3 Evidence in support of hypothesis 2: It was hypothesized that center of mass
displacement in the frontal plane will increase with increases in task complexity,
AND
Evidence in support of hypothesis 3: It was hypothesized that center of mass velocity
in the frontal plane will increase with increases in task complexity.
5.3.1 Total COM displacement and velocity
During human walking, the center of mass (COM) translates along the direction
of travel but also moves in a sinusoidal pattern in the vertical and lateral directions. In
both the vertical and lateral directions, there are two maximum peaks: the first near 30%
of the gait cycle in single-limb stance and another near 80% of the gait cycle in midswing; minimum peaks appear at 0% and 100% of the gait cycle in loading. Therefore,
the COM reaches its highest and most lateral point as it passes over the planted foot and
its lowest and most central point passing from one foot to the other. The motion is
produced by the horizontal shift of pelvis and relative adduction of hip.
We measured total COM excursion, which represents the difference, during the
gait cycle, between the most lateral position of the COM and the most medial position of
the COM. We choose to include this variable, because it has often been viewed as a very
sensitive measure of balance control during gait. Typically, it is expected that as balance
93
control during walking diminishes, greater COM excursion is observed. Thus, we
anticipated that the addition to the subject’s walking of secondary tasks (both cognitive
and motor) would result in increases in COM excursion.
As illustrated in Table 4.3 statistically, there was no difference in the total COM
excursion length in the frontal plane (p = .862) in our study across the task conditions.
Addition of a dual task did not appear to have an effect for this variable. This finding is
similar to that reported by Kurz et al. (2013)
[140]
who found no significant differences
in the COP excursion distance in the frontal plane, as well as no influence of cognitive
task on measures of frontal plane fluctuations, in an elderly population when they tested
them under a rapid voluntary stepping task. Our results are somewhat surprising, and fail
to support what was hypothesized. However, in view of the results for the variable of
step width, where there was no significant effect of step width across task conditions (p =
.946), these results appear to be predictable. It is through stepping laterally (i.e.
increasing step width) that the COM moves laterally. Since our subject’s step widths did
not change across task conditions, it follows that it is not likely that there would be an
increase in COM excursion.
Center of mass velocity in the frontal plane is a measure of the total distance that
the COM travels in the frontal plane during the gait cycle divided by the duration of the
gait cycle. It has been viewed as a particularly sensitive measure of medial/lateral
stability during gait. This is because, unlike COM excursion that reflects only the medial
and lateral COM positional extremes, it represents the interaction between the total
amount of movement in the frontal plane and the reaction to that. Thus, an individual
94
may have COM excursion values that are not particularly large, but relatively high COM
excursion velocity values, reflecting rapid movement in an attempt to control balance.
The mean center of mass velocity in the frontal plane for our subjects did not
change significantly with complexity of task as had been hypothesized (p = 0.104).
However, the trend in the data was consistent with the anticipated results. It did increase
by about 32% in the WCT condition, 3.2% in WC and 13% in the WT condition when
compared to the baseline W condition, but the changes were not sufficient to yield
statistical significance Our results are consistent with Raymakers et al. (2004) [130] who
studied an elderly population for center of pressure (COP) sway velocity on a force
platform with different cognitive tasks. They did not see any significant changes in the
velocity (ML) with addition of a secondary task. However, their study utilized quasistatic balance rather than dynamic balance in gait. As with other variables reflecting
medial lateral motion, it may be that the gait pattern imposed by our testing protocol
artificially constrained what, otherwise, would have been a normal method of
compensation.
5.3.2 Peak GRF in the frontal plane
In addition to the above balance variables, we also measured the peak mediolateral ground reaction force (GRF) over a single gait cycle across task conditions for our
subjects and normalized it to the body weight (BW). In general, GRF represents the
reaction that occurs in response to the forces against the ground that are created by the
subject. These forces represent both gravity acting on the mass of the subject and the
inertial forces resulting from the subject’s movements. Like step width, frontal plane
95
COM excursion, and frontal plane COM excursion velocity, medio-lateral GRF is
thought to be a reflection of frontal plane stability. Loss of frontal plane stability has
typically been associated with increases in medio-lateral GRF, as the subject attempts to
generate stabilizing torques through the application of force against the ground. In
contrast to what was hypothesized, we found that the peak GRF in the frontal plane
decreased (p = 0.011) as the task complexity increased. Upon further statistical analysis,
it was revealed that GRF in the frontal plane was different between the W and WC task
condition (11.1% more in WC, p = 0.009), W and WCT task condition (-11.1% less in
WCT, p = 0.027), and between WC and WT task conditions too (10% more in WT, p =
0.03). Interestingly, this is the first time where we found a significant difference between
the types of secondary concurrent task: motor vs. a cognitive type.
The peak medio-lateral GRF was a medially directed one, and it occurred, right
after heel strike, and prior to the midstance phase. Normal at heel strike, the GRF vector
is located lateral to the axis of the subtalar joint in the ankle, medial to the knee joint and
medial to the hip joint. During midstance, the GRF vector produces a pronation moment
at the subtalar joint, a varus moment at the knee and an adduction moment at the hip. The
normal body response to these moments is increased activity in the intrinsic foot muscles
in an attempt to supinate the subtalar joint at the ankle. At the knee, there is an increase in
the passive tension in the lateral knee structures. And at the hip, the abductor muscles are
activated. Collectively, the movements that these moments produce contribute to the
medio-lateral GRF that is recorded. When these moments must be increased to account
for medio-lateral instability, the result is increased medio-lateral GRF.
96
We found that for the W trials, subjects walked with an average peak medially
directed GRF of about 0.09 times their body weight (BW), and the lowest value was
found for the WCT trials, with an average of 0.08 times BW. This result can perhaps best
be explained by the fact that as gait speed decreased from the simple (W) to the most
complex task (WCT), the amount of GRF also decreased. This would apply to all
dimensions of GRF, and reflects the fact that decreasing velocity means decreased
momentum, which in turn decreases all three components of the GRF. If velocity had
remained constant across the conditions, it is more likely that the hypothesized trend in
the medio-lateral GRF would have been observed.
5.3.3 Conclusion
In conclusion, we can say that adding a dual task challenge did not perturb all
aspects of balance control in our subjects across the task conditions, but we did find it in
some aspects that changed predictably with increased task complexity. Statistically
significant decrease in the GRF produced in the ML direction, along with higher
velocities of the COM in the ML direction (though not statistically significant), provides
insight into the compromise of the balance control system as the task complexity
increased.
97
5.4 Evidence in support of hypothesis 4: 1. Verbal response time and accuracy will
decrease with increasing task complexity.
5.4.1 Response accuracy (RA) and response timing (RT)
In an attempt to examine if attention was challenged in the presence of
simultaneous the dual task conditions of the TUG, we evaluated the cognitive task of
counting backward by 3’s with two variables: 1) response accuracy (RA), calculated as #
of correct response / total # of responses, and 2) response time (RT), calculated as total #
of responses / time taken to complete the trial. RA was a measure of how many correct
responses the subject gave in a trial, and we studied it as a percentage. RT was a measure
to indicate how fast a person responded (how many numbers he/she counted), and we
studied it as numbers / unit of time (s). Analysis of these two variables provided an
indirect representation of the allocation of attention resources to the task, in the presence/
absence of another concurrent motor task.
Our results indicate that RA was about 87% for both WC and WCT conditions,
i.e. older adults performed with same accuracy level while counting backward with and
without balancing the tray. The results of the t-test revealed no significant differences
between the two cognitive task conditions (p = 0.897).
When it came to response timing (RT), the subjects counted an average of 4.77
total numbers in the counting trials (WC), and marginally higher (5.03 numbers) in the
counting and carrying the tray condition (WCT). The marginally higher numbers in the
WCT trials needs to be evaluated relative to the duration of task variable. When we look
at the duration they took to complete the task, subjects spent more time in the WCT trials
98
(9.4 s) and hence had more time to count numbers, as compared to the WC trial where
they completed the task at 8.66 s. Hence the higher number of total responses in the WCT
trials. The average value for RT in the WCT trials was 0.54 #s/s, where as it was
marginally lower in the WC trials (0.56 #s/s). A higher value would indicate better
efficiency with regard to cognitive task performance, and vice versa. This was not
statistically different (p = 0.579).
The explanation of dual-task interference is usually based on the assumption that
attention resources are limited [65]. According to this theoretical approach, dual-task
interference will only occur if the available central resource capacity is exceeded,
provoking a performance decrease in one or both tasks. Therefore, interference suggests
an overload of the central resources associated with an inability to appropriately adapt
allocation of attention between two simultaneously performed tasks. The manner in
which attention is divided between two tasks in dual-task paradigm mainly depends on
both the priority given (or not) to one task and the attentional load of each task. [39, 65]
5.4.2 Conclusion
In our study, subjects were asked to combine both walking and backward
counting with equal prioritizing to both the tasks, creating a condition in which attention
is divided. From the results of this study, we can see how certain characteristics of gait
were affected, at the cost of cognitive accuracy. Shumway-Cook et al. (2000) [44]
proposed a ‘posture-first hierarchy’ of task prioritization. The results of our study did not
support that hypothesis. Although participants were explicitly told to pay equal attention
to walking and cognitive tasks, they slowed their walking, while performance was
99
unaffected in their cognitive task trials. Our subjects exhibited a slow and cautious gait
pattern with increased duration to complete the trial, slower gait speed, shorter strides and
an increased double support phase as the task became complex. They also exhibited
reduced peak GRF in the frontal plane with increased COM velocity (though not
statistically significant) as the task complexity increased, suggesting that some elements
of balance were compromised.
5.5 Overall conclusions of the study
The study successfully examined 15 community dwelling independent
ambulators, between ages 65 and 88, scores of > 25 on MMSE test on their performance
of dual task conditions in an attempt to get an insight into their gait and dynamic balance
control system. We found that the most complex task situation of carrying the food tray
and counting backward had the most adverse effect on gait performance in our subjects
that resulted in 24% more time required to complete the TUG task, about 12% slowing of
gait, 40% increase in double support time and a 10% decrease in the generation of mediolateral ground reaction force, with conservation of cognitive task performance. However,
balance control parameters like center of mass excursion and velocity in the frontal plane
remained unaffected. The older adults in this study could proportionally change their
stride length, gait speed and double support time, as per the task demands.
The present findings underscore the importance of evaluating cognitive,
especially divided attention (assessment of executive function, EF), when trying to assess
for falls in older adults. A general clinical examination may fail to identify specific
100
causes of recurrent falls and some falls may therefore simply be attributed to aging. Gait
is an attention-demanding task, and any concurrent cognitive task, even a very simple
one, disrupts walking performance in this population. We recommend that identifying
older adults for falls should involve an intense evaluation of not just time taken to
complete a TUG test in a clinical setting, but should be backed by a thorough 3D
assessment of gait and balance parameters under various cognitive influences to bring out
potential destabilizing outcomes if any.
5.6 Limitations of the study
Despite equal priority instructions given to both tasks, based on the maintenance
of cognitive task performance, we can say that our subjects may have prioritized the
cognitive task in order not to make errors. Also, walking in combination with counting
backward by 3’s becomes rhythmic after a few trials, and may have resulted in the
cognitive task being performed accurately and quickly while gait slowed. We did try to
minimize this effect by randomizing the trials, but future studies can focus on different
and may be more challenging cognitive tasks. The current study was experimentally
designed with a modest motor task difficulty level (balancing a food tray with glass of
water, and fast walking), and our cognitive task was designed to study the effect of
divided attention on gait. We did not see greater allocation of attentional resources to gait
at the cost of cognitive task performance. An increase in cognitive task complexity may
have resulted in greater degradation of gait.
101
During human movement analysis there is also the potential for error in
estimating body motion. While the markers are placed on bony landmarks of the body,
estimations and assumptions are made in order to quantify body segment parameters.
Additionally, older adults who are obese will commonly have greater adipose tissue,
thereby making accurate placement of markers difficult, particularly those on the thorax
and pelvis. While skin motion artifact is a limitation to any marker based system, this
technology has been validated and tested in the researcher community.
Although this study provided interesting findings regarding gait characteristics in
the older adult population, several limitations do exist. First, the number of subjects in the
study may have limited the statistical power to detect biomechanical differences in the
elderly based on task conditions. Though we had statistically significant differences for
some gait parameters, but COM excursion and velocity in the frontal plane was low on
statistical power and effect size, resulting from a potential low sample size. Second,
almost all our subjects who volunteered for the study were highly functional and active
in the community and/or interested in examining their own balance ability. The ability to
generalize to all older adults still needs to be investigated. Further work should also be
conducted to quantify activity level among the elderly, and across a wide spectrum of
individuals based on age, gender, body composition, race and physical fitness level.
102
5.7 Future Work
Balance control and stability in the elderly remains a critical medical concern.
While balance control has been accepted as the ability to maintain the center of mass
within the base of support, there is still disagreement on proper quantification of
variability or stability. We found high inter subject variability in the balance parameters
across task conditions. In a traditional view of COM motion, greater variability was an
indicator of a degenerative system of posture control and increased risk of falling.
However, Hamill et al. (2006) [141] and colleagues propose that variability can also be
healthy and exploratory. A tradeoff between stability and variability might exist such that
once a stable posture can be achieved or recovered; the system may utilize variability as
an exploratory tool. Comparing variability during dual tasking condition in healthy and
balance impaired older adults might provide a better understanding of task specific,
compensatory adaptations.
An exercise program incorporating practice of executive function skills with
simultaneous motor skills can be implemented to see if this holistic approach brings an
improvement in gait, balance and cognitive parameters of balance impaired population. A
prospective study monitoring the effect of the exercise program on future falls in this
population would be ideal.
103
References
1.
Seidler, R.D., Bernard J.A., Burutolu1, T.B., Fling, B.W., Gordon, M.T., Gwin,
J.T., Kwak Y., Lipps, D.B., Motor control and aging: links to age-related brain
structural, functional, and biochemical effects. Neurosci Biobehav Rev, 2010.
34(5): p. 721-33.
2.
Lexell, J., Human aging, muscle mass, and fiber type composition. J Gerontol A
Biol Sci Med Sci, 1995. 50: p. 11-6.
3.
Hurley, B.F., Age, gender, and muscular strength. J Gerontol A Biol Sci Med Sci,
1995. 50: p. 41-4.
4.
Skinner, H.B., Barrack, R.L., Cook, S.D., Age-related decline in proprioception.
Clin Orthop Relat Res, 1984(184): p. 208-11.
5.
Stelmach, G.E.,Worringham, C.J., Sensorimotor deficits related to postural
stability. Implications for falling in the elderly. Clin Geriatr Med, 1985. 1(3): p.
679-94.
6.
Ochs AL, Lenhardt ML, Neural and vestibular aging associated with falls, in
Handbook of the Psychology of Aging, S.K. In Birren JE, Editor. 1985, Van
Nostrand Reinhold: New York. p. 278-299.
104
7.
Winter, D.A., Patla, A.E., Frank J.S., Walt, S.E., Biomechanical walking pattern
changes in the fit and healthy elderly. Phys Ther, 1990. 70(6): p. 340-7.
8.
Judge, J.O., Davis, R.B., Ounpuu, S., Step length reductions in advanced age: the
role of ankle and hip kinetics. J Gerontol A Biol Sci Med Sci, 1996. 51(6): p.
M303-12.
9.
Kerrigan, D.C., Todd, M.K., Croce, U.D., Lipsitz, L.A., Collins, J.J. ,
Biomechanical gait alterations independent of speed in the healthy elderly:
evidence for specific limiting impairments. Arch Phys Med Rehabil, 1998. 79(3):
p. 317-22.
10.
Elble, R.J., Thomas S.S., C. Higgins, J. Colliver., Stride-dependent changes in
gait of older people. J Neurol, 1991. 238(1): p. 1-5.
11.
Ostrosky, K.M., VanSwearingen, J.M., Burdett, R.G., Zena Gee, Z., A
comparison of gait characteristics in young and old subjects. Phys Ther, 1994.
74(7): p. 637-44; discussion 644-6.
12.
Ko, S.U., Hausdorff J.M., Ferrucci, L., Age-associated differences in the gait
pattern changes of older adults during fast-speed and fatigue conditions: results
from the Baltimore longitudinal study of ageing. Age Ageing, 2010. 39(6): p. 68894.
13.
Maki, B.E., McIlroy, W.E., The control of foot placement during compensatory
stepping reactions: does speed of response take precedence over stability? IEEE
Trans Rehabil Eng, 1999. 7(1): p. 80-90.
105
14.
Bauby, C.E., Kuo, A.D., Active control of lateral balance in human walking. J
Biomech, 2000. 33(11): p. 1433-40.
15.
Reichman, W.E., Cummings, J.L., Dementia. In: Duthie EH, Katz PR, Malone
ML, eds. Practice of Geriatrics. 4th ed. Philadelphia, PA: Elsevier Saunders;
2007: chap 25.
16.
Raz, N., Aging of the brain and its impact on cognitive performance: integration
of structural and functional findings. In: Craik FIM, Salthouse TA, editors. The
Handbook of Aging and Cognition. 2. Erlbaum; Mahwah, NJ: 2000. p. 1.
17.
A report of the Kellogg International Work Group on the Prevention of Falls by
the Elderly. Dan Med Bull. 1987;34(4): 1–24.
18.
Lord, S.R., Aging and falls: causes and prevention. J Musculoskelet Neuronal
Interact, 2007. 7(4): p. 347.
19.
WHO, Global Report on Falls Prevention in Older Age. 2007.
20.
Association, A.N., National Database for nursing quality indicators: Guidelines
for data collection and submission on quarterly indicators, version 5. 2005. p. 26.
21.
Zecevic, A.A., Salmoni, A.W., Speechley, M., Vandervoort, A.A., Defining a fall
and reasons for falling: comparisons among the views of seniors, health care
providers, and the research literature. Gerontologist, 2006. 46(3): p. 367-76.
22.
Rubenstein, L.Z., Josephson, K.R., Falls and their prevention in elderly people:
what does the evidence show? Med Clin North Am, 2006. 90(5): p. 807-24.
106
23.
Gray-Miceli D, J.J., Strumpf, N.E., A step-wise approach to a comprehensive
post-fall assessment. Annals of Long-Term Care: Clinical Care and Aging, 2005.
13(12): p. 16-24.
24.
Hausdorff, J.M., Rios, D.A., Edelberg, H.K., Gait variability and fall risk in
community-living older adults: a 1-year prospective study. Arch Phys Med
Rehabil, 2001. 82(8): p. 1050-6.
25.
Hornbrook, M.C., Stevens, V.J., Wingfield, D.J., Hollis, J.F., Greenlick, M.R.,
Preventing falls among community-dwelling older persons: results from a
randomized trial. Gerontologist, 1994. 34(1): p. 16-23.
26.
Nelson, R.C., Amin, M.A., Falls in the elderly. Emerg Med Clin North Am, 1990.
8(2): p. 309-24.
27.
Tinetti, M.E., Speechley, M., Ginter, S.F., Risk factors for falls among elderly
persons living in the community. N Engl J Med, 1988. 319(26): p. 1701-7.
28.
Centers for Disease Control and Prevention, National Center for Injury Prevention
and Control. Web–based Injury Statistics Query and Reporting System
(WISQARS) [online]. Accessed November 30, 2010.
29.
Tinetti, M.E., Baker, D.I., McAvay, G., Claus, E.B., A multifactorial intervention
to reduce the risk of falling among elderly people living in the community. N Engl
J Med, 1994. 331(13): p. 821-7.
30.
Campbell, A.J., Borne, M.J, Spears, G.F., Jackson, S.L., Brown, J.S., Fitzgerald,
J.L., Circumstances and consequences of falls experienced by a community
107
population 70 years and over during a prospective study. Age Ageing, 1990.
19(2): p. 136-41.
31.
Sattin, R.W., Falls among older persons: a public health perspective. Annu Rev
Public Health, 1992. 13: p. 489-508.
32.
Overstall, P.W., Exton-Smith, A.N., Imms, F.J., Johnson, A.L., Falls in the
elderly related to postural imbalance. Br Med J, 1977. 1(6056): p. 261-4.
33.
Ashley, M.J., Gryfe, C.I., Amies, A., A longitudinal study of falls in an elderly
population II. Some circumstances of falling. Age Ageing, 1977. 6(4): p. 211-20.
34.
Gabell, A., Simons, M.A., Nayak, U.S., Falls in the healthy elderly: predisposing
causes. Ergonomics, 1985. 28(7): p. 965-75.
35.
Prudham, D., Evans, J.G., Factors associated with falls in the elderly: a
community study. Age Ageing, 1981. 10(3): p. 141-6.
36.
Rubenstein, L.Z., Falls in older people: epidemiology, risk factors and strategies
for prevention. Age Ageing, 2006. 35 (2): p. 37-41.
37.
American Geriatrics Society, Geriatrics Society, American Academy Of,
Orthopaedic Surgeons Panel On Falls Prevention, Guideline for the prevention of
falls in older persons. American Geriatrics Society, British Geriatrics Society,
and American Academy of Orthopaedic Surgeons Panel on Falls Prevention. J
Am Geriatr Soc, 2001. 49(5): p. 664-72.
38.
Wright, D.L., Kemp, T.L.,The dual-task methodology and assessing the
attentional demands of ambulation with walking devices. Phys Ther, 1992. 72(4):
p. 306-12; discussion 313-5.
108
39.
Woollacott, M., Shumway-Cook, A., Attention and the control of posture and
gait: a review of an emerging area of research. Gait Posture, 2002. 16(1): p. 1-14.
40.
Bell, F., Principles of mechanics and biomechanics. 1998, Cheltenham, United
Kingdom: Stanley Thornes.
41.
Shumway-Cook, A., Woollacott, M.H., Motor Control:Theory and Practical
Applications, ed. W.W. Lippincott. 2001, Philadelphia.
42.
Horak, F.B., Clinical measurement of postural control in adults. Phys Ther, 1987.
67(12): p. 1881-5.
43.
Maki, B.E., McIlroy, W.E., The role of limb movements in maintaining upright
stance: the "change-in-support" strategy. Phys Ther, 1997. 77(5): p. 488-507.
44.
Shumway-Cook, A., Woollacott, M., Attentional demands and postural control:
the effect of sensory context. J Gerontol A Biol Sci Med Sci, 2000. 55(1): p. M106.
45.
Winter, D.A., Human balance and posture control during standing and walking.
Gait Posture, 1995. 3(4): p. 193-214.
46.
Schenkman, M., Interrelationship of neurological and mechanical factors in
balance control. proceedings of the APTA symposium on Balance, ed. P.W.
duncan. 1989, Nashville, Tn: APTA.
47.
Winter, D.A., Patla, A.E., Frank, J.S., Assessment of balance control in humans.
Med Prog Technol, 1990. 16(1-2): p. 31-51.
48.
Tinetti, M.E., Speechley, M., Prevention of falls among the elderly. N Engl J
Med, 1989. 320(16): p. 1055-9.
109
49.
Lee, D.N., Lishman, J.R., Visual proprioceptive control of stance. J Human
Movement Studies, 1975. 1: p. 87-95.
50.
Shumway-Cook, A., Woollacott, M., Motor Control; translating Research into
Clinical Practice. 3 ed. 2007, Philadelphia, Pa: Lippincott Williams & Wilkins.
51.
Nashner, L., Berthoz, A., Visual contribution to rapid motor responses during
postural control. Brain Res, 1978. 150(2): p. 403-7.
52.
Engstrom, H., Bergstrom, B., U. Rosenhall, U.,Vestibular sensory epithelia. Arch
Otolaryngol, 1974. 100(6): p. 411-8.
53.
Yankner, B.A., Lu, T., Loerch, P., The aging brain. Annu Rev Pathol, 2008. 3: p.
41-66.
54.
Williams, G.N., Higgins, M.J., Lewek, M.D., Aging skeletal muscle: physiologic
changes and the effects of training. Phys Ther, 2002. 82(1): p. 62-8.
55.
Patla, A.E., Frank, J.S., Winter, D.A., Balance control in the elderly: implications
for clinical assessment and rehabilitation. Can J Public Health, 1992. 83 Suppl 2:
p. S29-33.
56.
Ganz, D.A., Bao, Y., Shekelle, P., Rubenstein, L.Z., Will my patient fall? JAMA,
2007. 297(1): p. 77-86.
57.
Rubenstein, L.Z., Josephson, K.R., The epidemiology of falls and syncope. Clin
Geriatr Med, 2002. 18(2): p. 141-58.
58.
Teasdale, N., Bard, C., LaRue, J., Fleury, M., On the cognitive penetrability of
posture control. Exp Aging Res, 1993. 19(1): p. 1-13.
110
59.
Connell, B.R., Wolf, S.L., Environmental and behavioral circumstances
associated with falls at home among healthy elderly individuals. Atlanta FICSIT
Group. Arch Phys Med Rehabil, 1997. 78(2): p. 179-86.
60.
Verghese, J., Buschke, H., Viola, L., Katz, M., Hall, C., Kuslansky, G., Lipton,
R., Validity of divided attention tasks in predicting falls in older individuals: a
preliminary study. J Am Geriatr Soc, 2002. 50(9): p. 1572-6.
61.
Alais, D., Morrone, C., Burr, D., Separate attentional resources for vision and
audition. Proc Biol Sci, 2006. 273(1592): p. 1339-45.
62.
Kerr, B., Condon, S.M., McDonald, L.A., Cognitive spatial processing and the
regulation of posture. J Exp Psychol Hum Percept Perform, 1985. 11(5): p. 61722.
63.
Lajoie, Y., Teasdale, N., Bard, C., Fleury, M., Attentional demands for static and
dynamic equilibrium. Exp Brain Res, 1993. 97(1): p. 139-44.
64.
Shumway-Cook, A., Brauer, S., Woollacott, M., The effects of two types of
cognitive tasks on postural stability in older adults with and without a history of
falls. J Gerontol A Biol Sci Med Sci, 1997. 52(4): p. M232-40.
65.
Abernethy, B., Hanna, A., Plooy, A., The attentional demands of preferred and
non-preferred gait patterns. Gait Posture, 2002. 15(3): p. 256-65.
66.
Lindenberger, U., Marsiske, M., Baltes, P.B., Memorizing while walking:
increase in dual-task costs from young adulthood to old age. Psychol Aging,
2000. 15(3): p. 417-36.
111
67.
Kramer, A.F., Larish, J.F., Strayer, D.L., Training for attentional control in dual
task settings: A comparison of young and old adults. Experimental Psychology:
Applied, 1995. 1(1): p. 50-76.
68.
Kray, J., Lindenberger, U., Adult age differences in task switching. Psychol
Aging, 2000. 15(1): p. 126-47.
69.
Verhaeghen, P., Steitz, D.W., Sliwinski, M.J., Cerella, J., Aging and dual-task
performance: a meta-analysis. Psychol Aging, 2003. 18(3): p. 443-60.
70.
Kramer, A.F., Madden, D., Attention, in The handbook of aging and cognition,
F.I.M.C.T.A. Salthouse, Editor. 2008, Psychology Press: New York, NY.
71.
Salthouse, T.A., General and specific speed mediation of adult age differences in
memory. J Gerontol B Psychol Sci Soc Sci, 1996. 51(1): p. P30-42.
72.
Kahneman, D., Attention and effort, 1973, Englewood Cliffs, NJ: Prentice-Hall.
73.
Pashler, H.E., Processing stages in overlapping tasks:Evidence for a central
bottleneck. Experimental Psychology: Human Perception and Performance, 1984.
10: p. 358-377.
74.
Magill, R.A., Motor Learning: Concepts and Application. 6th ed. 2001, New
York, NY: McGraw-Hill.
75.
McCulloch, K., Attention and dual-task conditions: physical therapy implications
for individuals with acquired brain injury. J Neurol Phys Ther, 2007. 31(3): p.
104-18.
76.
Lezak, M.D., Neuropsychological assessment. 1995, New York, NY: Oxford
University Press.
112
77.
Stuss, D.T., Levine, B., Adult clinical neuropsychology: lessons from studies of
the frontal lobes. Annu Rev Psychol, 2002. 53: p. 401-33.
78.
Fuster, J.M., Synopsis of function and dysfunction of the frontal lobe. Acta
Psychiatr Scand Suppl, 1999. 395: p. 51-7.
79.
Goethals, I., Audenaert, K., Van de Wiele, C., Dierckx, R.., The prefrontal cortex:
insights from functional neuroimaging using cognitive activation tasks. Eur J
Nucl Med Mol Imaging, 2004. 31(3): p. 408-16.
80.
Collette, F., Hogge, M., Salmon, E., Van der Linden, M., Exploration of the
neural substrates of executive functioning by functional neuroimaging.
Neuroscience, 2006. 139(1): p. 209-21.
81.
Lorenz-Reuter, P.A., Cognitive neuropsychology of the aging brain. Cognitive
aging: a primer, ed. S.N. Park DC. 2000, Philadelphia, PA: Psychology Press,
Taylor & Francis.
82.
Craik, F.I., Grady, C.L., Aging, memory and frontal lobe functioning, in
Principles of frontal lobe function, D.T. Stuss and K. R.T., Editors. 2002, Oxford
University Press: New York, NY. p. 528-540.
83.
Ble, A., Volpato, S., Zuliani, G., Executive function correlates with walking speed
in older persons: the InCHIANTI study. J Am Geriatr Soc, 2005. 53(3): p. 410-5.
84.
Cocchini, G., Della Sala, S., Logie, R.H., Pagani, R., Dual task effects of walking
when talking in Alzheimer's disease. Rev Neurol (Paris), 2004. 160(1): p. 74-80.
85.
Verhaeghen, P., Cerella, J., Aging, executive control, and attention: A review of
meta-analyses. Neurosci Biobehav Rev, 2002. 26: p. 849-857.
113
86.
Di Fabio, R.P., Zampieri, C., Tuite, P., Footlift asymmetry during obstacle
avoidance in high-risk elderly. J Am Geriatr Soc, 2004. 52(12): p. 2088-93.
87.
Lord, S.R., Fitzpatrick, R.C., Choice stepping reaction time: a composite measure
of falls risk in older people. J Gerontol A Biol Sci Med Sci, 2001. 56(10): p.
M627-32.
88.
Rapport, L.J., Hanks, R.A., Millis, S.R., Executive functioning and predictors of
falls in the rehabilitation setting. Arch Phys Med Rehabil, 1998. 79(6): p. 629-33.
89.
Shumway-Cook, A., Brauer, S., Woollacott, M., Predicting the probability for
falls in community-dwelling older adults using the Timed Up & Go Test. Phys
Ther, 2000. 80(9): p. 896-903.
90.
Lundin-Olsson, L., Nyberg, L., Gustafson, Y.,"Stops walking when talking" as a
predictor of falls in elderly people. Lancet, 1997. 349(9052): p. 617.
91.
Lundin-Olsson, L., Nyberg, L., Gustafson, Y., Attention, frailty, and falls: the
effect of a manual task on basic mobility. J Am Geriatr Soc, 1998. 46(6): p. 75861.
92.
Faulkner, K.A., Redfern, M.S., Cauley, J.A., Multitasking: association between
poorer performance and a history of recurrent falls. J Am Geriatr Soc, 2007.
55(4): p. 570-6.
93.
Melzer, I., Kurz, I., Shahar, D., Application of the voluntary step execution test to
identify elderly fallers. Age Ageing, 2007. 36(5): p. 532-7.
94.
Beauchet, O., Dubost, V., Herrmann, F., Rabilloud, M., Gonthier, R., Kressig,
R.W., Relationship between dual-task related gait changes and intrinsic risk
114
factors for falls among transitional frail older adults. Aging Clin Exp Res, 2005.
17(4): p. 270-5.
95.
Hausdorff, J.M., Yogev, G., Springer, S., Simon, E.S., Giladi, N., Walking is
more like catching than tapping: gait in the elderly as a complex cognitive task.
Exp Brain Res, 2005. 164(4): p. 541-8.
96.
Hall, C.D., Echt, K.V., Wolf, S..L, Rogers, W.A., Cognitive and motor
mechanisms underlying older adults' ability to divide attention while walking.
Phys Ther, 2011. 91(7): p. 1039-50.
97.
van Iersel, M.B., Ribbers, H.,Munneke, M.,Borm, G.F.,Rikkert,M.G., The effect
of cognitive dual tasks on balance during walking in physically fit elderly people.
Arch Phys Med Rehabil, 2007. 88(2): p. 187-91.
98.
Hollman, J.H., Kovash, F.M., Kubik, J.J., Linbo, R.A., Age-related differences in
spatiotemporal markers of gait stability during dual task walking. Gait Posture,
2007. 26(1): p. 113-9.
99.
Beauchet, O., Dubost, V., Herrmann, F.R., Kressig, R.W., Stride-to-stride
variability while backward counting among healthy young adults. J Neuroeng
Rehabil, 2005. 2: p. 26.
100.
Shkuratova, N., Morris,M.E., Huxham, F.,Effects of age on balance control
during walking. Arch Phys Med Rehabil, 2004. 85(4): p. 582-8.
101.
Maki, B.E., Whitelaw, R.S. Influence of expectation and arousal on center-ofpressure responses to transient postural perturbations. J Vestib Res, 1993. 3(1):
p. 25-39.
115
102.
Chen, H.C., Schultz, A.B., Ashton-Miller, J.A., Giordani, B., Alexander, N.B.,
Stepping over obstacles: dividing attention impairs performance of old more than
young adults. J Gerontol A Biol Sci Med Sci, 1996. 51(3): p. M116-22.
103.
Camicioli, R., Oken, B.S., Sexton, G., Kaye, J.A., Nutt, J.G., Verbal fluency task
affects gait in Parkinson's disease with motor freezing. J Geriatr Psychiatry
Neurol, 1998. 11(4): p. 181-5.
104.
Brown, R.G., Marsden, C.D., Dual task performance and processing resources in
normal subjects and patients with Parkinson's disease. Brain, 1991. 114 ( Pt 1A):
p. 215-31.
105.
Bond, J.M., Morris, M., Goal-directed secondary motor tasks: their effects on gait
in subjects with Parkinson disease. Arch Phys Med Rehabil, 2000. 81(1): p. 1106.
106.
Pettersson, A.F., Olsson, E., Wahlund, L.O., Effect of divided attention on gait in
subjects with and without cognitive impairment. J Geriatr Psychiatry Neurol,
2007. 20(1): p. 58-62.
107.
Manckoundia, P., Pfitzenmeyer, P., d'Athis, P., Dubost, V., Mourey, F., Impact of
cognitive task on the posture of elderly subjects with Alzheimer's disease
compared to healthy elderly subjects. Mov Disord, 2006. 21(2): p. 236-41.
108.
Krebs, D.E., Scarborough, D.M., McGibbon, C.A., Functional vs. strength
training in disabled elderly outpatients. Am J Phys Med Rehabil, 2007. 86(2): p.
93-103.
116
109.
Bayouk, J.F., Boucher, J.P., Leroux, A.,Balance training following stroke: effects
of task-oriented exercises with and without altered sensory input. Int J Rehabil
Res, 2006. 29(1): p. 51-9.
110.
Salbach, N.M., Mayo, N.E., Robichaud-Ekstrand, S., Hanley, J.A., Richards,
C.L., and Wood-Dauphinee,S., The effect of a task-oriented walking intervention
on improving balance self-efficacy poststroke: a randomized, controlled trial. J
Am Geriatr Soc, 2005. 53(4): p. 576-82.
111.
Pellecchia, G.L., Dual-task training reduces impact of cognitive task on postural
sway. J Mot Behav, 2005. 37(3): p. 239-46.
112.
Li, K.Z., Roudaia, E., Lussier, M., Bherer, L., Leroux ,A., Benefits of cognitive
dual-task training on balance performance in healthy older adults. J Gerontol A
Biol Sci Med Sci, 2010. 65(12): p. 1344-52.
113.
Silsupadol, P., Shumway-Cook, A., Lugade, V., van Donkelaar, P., Chou, L.S.,
Mayr, U., Woollacott, M.H., Effects of single-task versus dual-task training on
balance performance in older adults: a double-blind, randomized controlled trial.
Arch Phys Med Rehabil, 2009. 90(3): p. 381-7.
114.
Plummer-D'Amato, P., Cohen, Z., Daee, N.A., Lawson, S.E., Lizotte, M.R.,
Padilla, A., Effects of once weekly dual-task training in older adults: a pilot
randomized controlled trial. Geriatr Gerontol Int, 2012. 12(4): p. 622-9.
115.
MacKinnon, C.D., Winter, D.A., Control of whole body balance in the frontal
plane during human walking. J Biomech, 1993. 26(6): p. 633-44.
117
116.
Lajoie, Y., Gallagher, S.P., Predicting falls within the elderly community:
comparison of postural sway, reaction time, the Berg balance scale and the
Activities-specific Balance Confidence (ABC) scale for comparing fallers and
non-fallers. Arch Gerontol Geriatr, 2004. 38(1): p. 11-26.
117.
Vaillant, J., Prediction of falls with performance on Timed "Up-and-Go" and oneleg-balance tests and additional cognitive task. Ann Readapt Med Phys, 2006.
49(1): p. 1-7.
118.
Hyndman, D., Ashburn, A., Stops walking when talking as a predictor of falls in
people with stroke living in the community. J Neurol Neurosurg Psychiatry, 2004.
75(7): p. 994-7.
119.
Podsiadlo, D., Richardson, S., The timed "Up & Go": a test of basic functional
mobility for frail elderly persons. J Am Geriatr Soc, 1991. 39(2): p. 142-8.
120.
Mathias, S., Nayak, U.S., Isaacs, B., Balance in elderly patients: the "get-up and
go" test. Arch Phys Med Rehabil, 1986. 67(6): p. 387-9.
121.
Steffen, T.M., Hacker, T.A., Mollinger, L., Age- and gender-related test
performance in community-dwelling elderly people: Six-Minute Walk Test, Berg
Balance Scale, Timed Up & Go Test, and gait speeds. Phys Ther, 2002. 82(2): p.
128-37.
122.
Dite, W., Temple, V.A., Development of a clinical measure of turning for older
adults. Am J Phys Med Rehabil, 2002. 81(11): p. 857-66; quiz 867-8.
118
123.
Kaya, B.K., Krebs, D.E., Riley, P.O., Dynamic stability in elders: momentum
control in locomotor ADL. J Gerontol A Biol Sci Med Sci, 1998. 53(2): p. M12634.
124.
Winter, D.A., Eng, P., Kinetics: our window into the goals and strategies of the
central nervous system. Behav Brain Res, 1995. 67(2): p. 111-20.
125.
Lee, H.J., Chou, L.S., Detection of gait instability using the center of mass and
center of pressure inclination angles. Arch Phys Med Rehabil, 2006. 87(4): p.
569-75.
126.
Silsupadol, P., Lugade, V., Shumway-Cook, A., van Donkelaar, P., Chou, L.S.,
Mayr, U., Training-related changes in dual-task walking performance of elderly
persons with balance impairment: a double-blind, randomized controlled trial.
Gait Posture, 2009. 29(4): p. 634-9.
127.
Chen, C.J., Chou, L.S., Center of mass position relative to the ankle during
walking: a clinically feasible detection method for gait imbalance. Gait Posture,
2010. 31(3): p. 391-3.
128.
Maki, B.E., Holliday,P.J., Fernie, G.R.,Aging and postural control. A comparison
of spontaneous- and induced-sway balance tests. J Am Geriatr Soc, 1990. 38(1):
p. 1-9.
129.
Lafond, D., Intrasession reliability of center of pressure measures of postural
steadiness in healthy elderly people. Arch Phys Med Rehabil, 2004. 85(6): p. 896901.
119
130.
Raymakers, J.A., Samson, M.M. and Verhaar, H.J., The assessment of body sway
and the choice of the stability parameter(s). Gait Posture, 2005. 21(1): p. 48-58.
131.
Krebs, D.E., McGibbon, C.A.and Goldvasser, D., Analysis of postural
perturbation responses. IEEE Trans Neural Syst Rehabil Eng, 2001. 9(1): p. 7680.
132.
Hahn, M.E., Chou, L.S. , Age-related reduction in sagittal plane center of mass
motion during obstacle crossing. J Biomech, 2004. 37(6): p. 837-44.
133.
Chou, L.S., Kaufman, K.R., Hahn, M.E., Brey, R.H., Medio-lateral motion of the
center of mass during obstacle crossing distinguishes elderly individuals with
imbalance. Gait Posture, 2003. 18(3): p. 125-33.
134.
Folstein, M.F., Folstein, S.E., McHugh, P.R.,"Mini-mental state". A practical
method for grading the cognitive state of patients for the clinician. J Psychiatr
Res, 1975. 12(3): p. 189-98.
135.
Beurskens, R., Bock, O., Age-related deficits of dual-task walking: a review.
Neural Plast, 2012. 2012: p. 131608.
136.
Coppin, A.K., Shumway-Cook, A., Saczynski1, J.S., Patel, K.V., Ble, A.,Ferrucci,
L., Guralni, J.M., Association of executive function and performance of dual-task
physical tests among older adults: analyses from the InChianti study. Age
Ageing, 2006. 35(6): p. 619-24.
137.
Hernandez, A., Silder, A., Heiderscheit, B.C., Thelen, D.G., Effect of age on
center of mass motion during human walking. Gait Posture, 2009. 30(2): p. 21722.
120
138.
Priest, A.W., Salamon, K.B., Hollman, J.H., Age-related differences in dual task
walking: a cross sectional study. J Neuroeng Rehabil, 2008. 5: p. 29.
139.
Gabell, A., Nayak, U.S., The effect of age on variability in gait. J Gerontol, 1984.
39(6): p. 662-6.
140.
Kurz, I., Berezowski, E., Melzer, I., Frontal Plane Instability Following Rapid
Voluntary Stepping: Effects of Age and a Concurrent Cognitive Task. J Gerontol
A Biol Sci Med Sci, 2013.
141.
Hamill, J., Haddad, J.M., Heiderscheit, B.C., Emmerik, R.E. A., Movement
System Variability. Human Kinetics: Champaign. 2006. 153-165.
121
Appendix A
IRB approved flyer for subject recruitment
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Appendix B
Informed consent form
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Appendix C
Data collection sheet
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