and quantitative gait parameters in community-dwelling stroke survivors

The association between qualitative- and

quantitative gait parameters in

community-dwelling stroke survivors

Masterthesis

Physiotherapy Science

Program in Clinical Health Sciences

Utrecht University

Name student: C. (Carllijn) Otten

Student number: 5668654

Date: 30 June 2017

Internship supervisor(s): dr. I. van de Port, M. Punt, MSc

Internship institute: Revant, rehabilitation centre of Breda

Lecturer/supervisor Utrecht University: dr. M.F. Pisters

Otten C. (Carlijn) The association between qualitative- and quantitative gait parameters in stroke survivors

2

“ONDERGETEKENDE

Carlijn Otten

bevestigt hierbij dat de onderhavige verhandeling mag worden geraadpleegd en vrij mag

worden gefotokopieerd. Bij het citeren moet steeds de titel en de auteur van de verhandeling

worden vermeld.”


3

Examiner

Dr. M.F. Pisters

Assessors:

Dr. I van de Port

Dr. J. van der Net

Masterthesis, Physical Therapy Sciences, Program in Clinical Health Sciences, Utrecht

University, Utrecht, 2017


4

Abstract

Aim: This study aims to explore the association of qualitative gait parameters with quantity

of gait in community-dwelling stroke survivors.

Methods: In this cross-sectional study 55 community-dwelling stroke survivors were

measured for seven consecutive days by accelerometry to determine quantity- and quality of

gait. Dependent variables of this study were quantitative gait parameters, i.e. the number of

walking bouts per 24 hours and the total gait activity time per 24 hours. Independent

variables were qualitative gait parameters, i.e. gait speed, stride time, gait symmetry,

smoothness of gait and gait variability. Univariate- and multivariate regression analysis were

used to explore the possible related qualitative- and quantitative gait parameters.

Results: Based on the univariate regression analysis and tests for multicollinearity, six

variables were included for multiple regression analysis with gait activity time as dependent

variable. By using backward selection in this multiple regression model, only gait speed

remains in the final model, with R2 of 0.126. Five variables were included in the multiple

regression analysis with the number of walking bouts as dependent variable. By using

backward selection in this multiple regression model, only the index of harmonicity in vertical

direction remains in the final model, with R2 of 0.091.

Conclusion: This study showed that smoothness of gait (index of harmonicity in vertical

direction) and gait speed were significantly related to quantity of gait, corrected for other

variables. However these qualitative gait parameters explained very little of the variance in

quantity of gait. Possibly other factors, such as emotional, social and environmental factors

have more influence on quantity of gait in this population.

Clinical Relevance: This study showed quantity of gait can’t be explained that much by

quality of gait. So further research needed to be broader focused, not only to physical

aspects, but also to social and emotional factors that can influence quantity of gait. Possibly

these other factors should be more clinical relevance than the qualitative gait parameters

used in this study.

Keywords: stroke, gait quantity, gait quality, accelerometry, physical activity


5

Introduction

Each year 15 million people worldwide suffer from a stroke.1 Nearly six million of them die

and another five million are left permanently disabled.1 Stroke is the second leading cause of

disability, after dementia.1 Impact of a stroke depends primarily on the location of the

obstruction or bleeding, but the most common symptoms after stroke are paralyses of one

side of the body, speech and language problems, memory loss, fatigue and changes in

behavioral style.2 The hemiplegia that frequently follows a stroke often reduces the patient’s

ability to walk.3 Therefore, independent walking is a common rehabilitation goal in patients

with stroke as being a key to independence in daily functioning.4

Physical activity protects against a multitude of chronic health problems including many

forms of cardiovascular disease.5 Furthermore, there is emerging evidence that regular

physical activity and exercise in stroke survivors has a beneficial impact on risk factors for

cardiovascular diseases.6 Stroke survivors however spent more time sitting and less time in

activity than their peers.7,8 Also the intensity and duration of physical activity are generally

low in stroke survivors.9

Besides that stroke survivors are less physically active, also the quality of gait is different in

stroke survivors compared to healthy individuals. Stroke survivors have a more

asymmetrical10 and unstable11 gait. Different studies found significant differences in gait

speed12,13,14, step regularity13, step length14 and gait symmetry10 between stroke survivors and

their healthy peers. Possibly there is a relation between these qualitative gait parameters and

the quantity of gait. Several studies studied the associations between the described

qualitative gait parameters with each other, for example they found significant correlation

between gait asymmetry and gait speed.15 In addition, gait speed8,16,12, walking capacity

(measured by 6-minutes walking test)16, balance (measured by Berg Balanced Scale)16 and

physical fitness (peak oxygen uptake)16 were found to be positively related to the quantity of

gait as expressed in steps per day. Also, motor function of the lower extremity (measured by

Motricity Index), balance (measured by Berg Balance Scale), gait speed, gait distance

(measured by 6-minutes walking test) and the ability to increase speed are significantly

related to self-reported community ambulation levels.4,17 No studies, however, were found

that studied the associations between the quantitative- and qualitative gait parameters

measured by accelerometry in the home-setting of stroke survivors.

Therefore, this study aims to explore the association of qualitative gait parameters with

quantity of gait in community-dwelling stroke survivors. When an association will be found

between qualitative gait parameters and the quantity of gait, interventions can be more

specifically focused to the related parameters, aiming to positively influence the level of

physical activity.


6

Methods

Study design and setting

This study is a cross-sectional study in chronic stroke survivors, living in the community. Data

were collected between March 2013 and June 2016, in region Breda and Utrecht, the

Netherlands.

Participants

The population consisted of community-dwelling stroke survivors. These persons were

recruited at local physical therapy centres and national peer group meetings in region Breda

and Utrecht, the Netherlands. Participants were included if they had a minimum age of 18

years old; were at least 6 months post-stroke; lived independently in the community and

were able to walk independently for at least 20 meters, if necessary with a walking aid.

Exclusion criteria were a Functional Ambulation Category (FAC) 2 or less and severe cognitive

disorders, as indicated by a Minimal Mental State Examination (MMSE) score of 24 or less.18

Data measurement and procedure

All dependent and independent variables were measured by a tri-axial accelerometer. The

accelerometer consisted of hardware from McRoberts with the FESTA (FEedback to STimulate

Activity) software, developed and validated by Punt et al. (2014).19 Results of criterion validity

(compared to video analysis) and test-retest reliability of the FESTA algorithm indicated good

validity and reliability as all ICC values were between 0.841 and 0.972.19 The level of

acceleration or deceleration was registered 100 times per second by the accelerometer and

the data was digitally stored on a mini SD card. Gait activity was identified from the seven

days using the validated algorithm for gait detection and gait quantification.19 Algorithms

were used to determine the qualitative gait parameters from the trunk acceleration data.20

For each parameter, the median value over all registered time of a participant was used for

statistical evaluation. The median value has been taken, because the median is less sensitive

in comparison to the mean for outliers in the estimated gait parameter.21

Participants received the accelerometer and instruction from the researcher. They were asked

to wear the tri-axial accelerometer at the middle of the lower back using an elastic belt.20

Participants were instructed to wear the accelerometer for seven consecutive days, preferably

during day and night, but were allowed to take it off when going to bed. The accelerometer

was removed during showering and other water related activities to prevent damage. After

one week, participants realign the accelerometer and send this by post to the researcher. The

researcher analysed the data from the mini SD-card.

Variables

Dependent variables

The main study parameter was quantity of gait, which was operationalized in two parameters:


7

the number of walking bouts per 24 hours and the total gait activity time per 24 hours.

The number of walking bouts described how often a person walks per 24 hours, the

accelerometer registered a new walking bout during gait when the person stops walking for

one second or more. Only walking bouts of minimum eight seconds were stored. The total

gait activity time reflected how many minutes a person walks in total per 24 hours. Because

both variables described a different part of quantity of gait, both parameters were included,

whereby the activity behavior of the stroke survivor could be reflected as good as possible.

Independent variables

All independent variables, even as the dependent variables, were measured by a tri-axial

accelerometer

- Gait speed (m/s), the distance a person can walk in one second. Measured by dividing

the distance (m) to the time (s) a person walks.

- Stride time (seconds), the time the person needs for one stride. A stride is two steps.

- Gait symmetry is determined by the harmonic ratio (HR). The harmonic ratio is based

on the premise that the unit of measurement from a continuous walking trial is a

stride. A stable, rhythmic gait pattern should therefore consist of acceleration patterns

that repeat in multiples of two within any given stride, as these patterns are therefore

‘completed’ before taking subsequent strides. Acceleration patterns that do not

repeat in multiples of two are problematic, as they produce out of phase accelerations

that are not completed within each stride, and therefore manifest as irregular

accelerations during a walking trial. The components of the acceleration signal that

are ‘in phase’ (the even harmonics) are compared to the components that are ‘out of

phase’ (odd harmonics). A harmonic ratio is calculated by dividing the sum of the

amplitudes of the first ten even harmonics by the sum of the amplitudes of the first

ten odd harmonics. Symmetrical gait in the vertical (VT) and anterior-posterior (AP)

direction will predominantly contain even harmonics which will result in a higher HR.22

The HR for the mediolateral direction is calculated as the sum of the amplitudes of

the odd-numbered harmonics divided by the sum of the amplitudes of the even-

numbered harmonics. Therefore symmetrical gait in the ML direction will result in a

lower HR.22

- Smoothness of gait is determined by the Index of Harmonicity (IH). This IH measure

divides the ground frequency (first harmonic) of the time series by the first six

harmonics of the time series. A complete smooth gait can be described by one

sinusoidal function and no higher harmonics would be necessary to describe the

signal. So a smooth gait would result in a higher IH value.23


8

- Gait variability quantifies the consistency and rhythmicity of stepping, determined by

three parameters:

o The amplitude of the dominant peak of the power spectrum, which represents

the ‘strength’ of the dominant peak relative to the rest of the signal. When

people walk at different rhythms/frequencies than this value would be low, so

they have a high gait variability. Otherwise, a high value represents a low

variability and so a more consistent gait pattern.24

o The width of peak of the power spectrum, which reflects the dispersion of the

dominant peak. A higher value represents a higher variability and so a less

consistent gait pattern. 24

o The local dynamic stability expressed as the local divergence exponent (LDE),

which quantifies the exponential rate of divergence from initially nearby

kinematics states as a function of stride time.20 A higher LDE indicates a faster

diverging acceleration signal and indicates a more unstable gait pattern, so a

higher gait variability.

Except for gait speed and stride time all these independent variables were determined in

three directions: the vertical direction (VT), the anterior-posterior direction (AP) and the

medio-lateral direction (ML).

Statistical methods

First of all, data was checked for outliers by using descriptive statistics and visual inspection

of histograms. Descriptive statistics were used to describe the sample and were reported as

mean ± standard deviation.

To use linear regression analysis the following assumptions were confirmed. The linearity

between the dependent and independent variables were tested by using scatterplots and the

test for linearity. Normality was tested by making a Q-Q plot and using the Kolmogorov-

Smirnov test. Homoscedasticity was checked by using a scatterplot. Autocorrelation was

checked by using a scatterplot and by using the Durbin-Watson test with a level of

significance of 0.05, whereby 2 means no autocorrelation, 0-2 means positive autocorrelation,

2-4 means negative autocorrelation. When the assumptions of linearity and normality were

not confirmed for the dependent variables, logistics transformations for these variables were

used.

Univariate regression analyses were used to determine possible related qualitative gait

parameters with quantity of gait (number of walking bouts and total gait activity time).

Variables that were significantly associated with quantity of gait (p<0.2) were checked for

multicollinearity. When two variables were highly correlated (r>0.7), only the variable with the

highest correlation with the dependent variable was included in the multivariate regression

analysis. In this final multiple regression analysis, backward selection was used, the criterion

for remaining in the model was p=0.05. Two regression models were made: one time with the

number of walking bouts as dependent variable, and one time with the total gait activity time


9

as dependent variable. The qualitative gait parameters were used as independent variables.

The fit of the final models were assessed using R2. All statistical analyses were performed

using SPSS 21.0.


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Results

In total data of 55 stroke survivors was included in this study, of these 25 females and 30

males with a mean age of 66 years. The mean gait activity time of these people was 35

minutes a day and the mean number of walking bouts was 127 times a day. Table 1 gives an

overview of all patient- and gait characteristics of the study population.

Table 1: characteristics of participants

Mean ± SD

Patients

characteristics

Female/male

Age (years)

Height (cm)

Weight (kg)

BMI (kg/m2)

25/30

66,14 ± 10,85

171,67 ± 8,95

81,12 ± 15,67

27,53 ± 5,19

Gait

characteristics

Quantity of gait

Walking Bouts

Gait activity time (m)

126,79 ± 70,42

35,2 ± 21,98

Quality of gait

Gait speed (m/s)

Stride time (s)

Index of harmonicity VT

Index of harmonicity ML

Index of harmonicity AP

Harmonic Ratio VT

Harmonic Ratio ML

Harmonic Ratio AP

Width of peak VT

Width of peak ML

Width of peak AP

Dominance of peak VT

Dominance of peak ML

Dominance of peak AP

Local divergence

exponent VT

Local divergence

exponent ML

Local divergence

exponent AP

0,71 ± 0,16

1,33 ± 0,33

0,44 ± 0,17

0,44 ± 0,23

0,52 ± 0,15

1,26 ± 0,26

1,34 ± 0,17

1,13 ± 0, 22

1,01 ± 0,14

0,95 ± 0,02

0,95 ± 0,02

1,98 ± 0,50

1,47 ± 1,25

1,32 ± 0,36

1,05 ± 0,34

0,94 ± 0,29

0,97 ± 0,48


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Gait activity time

Table 2 gives an overview of the univariate- and multivariate linear regression analysis with

the total gait activity time as dependent variable. In the univariate analysis gait speed, stride

time, index of harmonicity VT, width of peak VT, dominance of peak VT, dominance of peak

AP, LDE VT and LDE AP showed statistically significant results (p<0.20). After excluding

variables due to multicollinearity, finally six variables were selected in the multivariate

analysis: gait speed, stride time, dominance of peak VT, dominance of peak AP, width of peak

VT and width of peak AP (Appendix 1). By using backward selection in the multiple regression

model only the gait speed remained in the final model, with R2 of 0.126.

Table 2: regression analysis with gait activity as dependent variable.

Independent variables B (standardized)

P

B (standardized)

P

Gait speed 0.355 0.008 0.355 0.008

Stride time -0.258 0.057

Index of harmonicity VT 0.323 0.016

Index of harmonicity ML -0.167 0.223

Index of harmonicity AP 0.021 0.880

Harmonic Ratio VT 0.104 0.451

Harmonic Ratio ML -0.066 0.633

Harmonic Ratio AP 0.183 0.182

Width of peak VT -0.316 0.019

Width of peak ML -0.046 0.736

Width of peak AP -0.185 0.175

Dominance of peak VT -0.206 0.132

Dominance of peak ML 0.117 0.395

Dominance of peak AP 0.237 0.081

Local divergence

exponent VT

-0.199 0.145

Local divergence

exponent

-0.136 0.324

Local divergence

exponent AP

-0.213 0.118

Univariate analysis Multivariate analysis


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Number of walking bouts

Table 3 gives an overview of the univariate- and multivariate linear regression analysis with

the number of walking bouts as dependent variable. In the univariate analysis walking speed,

stride time, index of harmonicity VT, harmonic ratio AP, width of peak VT, width of peak AP,

dominance of peak VT, dominance of peak AP, LDE VT and LDE AP had significant regression

coefficients (p<0.20). After excluding variables due to multicollinearity, finally five variables

were selected in the multivariate analysis: stride time, index of harmonicity VT, dominance of

peak VT, dominance of peak AP, width of peak VT (Appendix 1). By using backward selection

in the multiple regression model only the index of harmonicity VT remained in the final

model, with R2 of 0.091.

.

Independent variables B (standardized)

P

B (standardized)

P

Gait speed 0.266 0.050

Stride time -0.254 0.061

Index of harmonicity VT 0.302 0.025 0.302 0.025

Index of harmonicity ML -0.058 0.672

Index of harmonicity AP 0.034 0.803

Harmonic Ratio VT -0.005 0.968

Harmonic Ratio ML -0.002 0.989

Harmonic Ratio AP 0.039 0.779

Width of peak VT -0.201 0.142

Width of peak ML -0.042 0.759

Width of peak AP -0.017 0.904

Dominance of peak VT -0.179 0.190

Dominance of peak ML -0.042 0.759

Dominance of peak AP 0.197 0.150

Local divergence

exponent VT

-0.194 0.156

Local divergence

exponent ML

-0.103 0.456

Local divergence

exponent AP

-0.194 0.155

Table 3: regression analysis with walking bouts as dependent variable

Univariate analysis Multivariate analysis


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Discussion

This study showed the index of harmonicity in VT direction is significantly related to the

number of walking bouts and gait speed is significantly related to the total gait activity time,

after correcting for other variables, in community-dwelling stroke survivors. So in this study it

seems that stroke survivors with a smoother gait pattern walked more times a day and gait

speed is positively related to the total time a person walked a day.

In line with these results, an earlier study showed that slower gait speed is associated with

more time spent sitting, and in particular more time spent in prolonged bouts of sitting, as

well as less time in moderate to vigorous physical activity.8 Gait speed was also found to be

significantly associated with steps per day16, overall walking time12 and community

ambulation4,25. These studies measured gait speed by a five or ten meters walking test.

However in this study gait speed was measured by accelerometry during seven days in daily

life and quantity of gait was measured at the same time using the same accelerometer. So

possibly these method represent more the real gait speed persons walk with. The index of

harmonicity in VT direction seems an important related factor to quantity of gait in this study.

However no other studies were found that studied this variable related to quantity of gait.

This variable has been studied in relation to fall incidents in stroke survivors and this showed

that index of harmonicity in VT direction was significantly associated to history of falls.21 So it

seems that smoothness of gait is related to quantity of gait, as well as to fall history in this

population.

The qualitative gait parameters explained, however, very little of the variance in quantity of

gait. The index of harmonicity in VT direction explained only nine percent of the variance in

the number of walking bouts, and gait speed explained only thirteen percent of the variance

in the total gait activity time. There is still a lot of unexplained variance in quantity of gait, so

there should be other factors, for example environmental, social, emotional and

psychological factors with more influence on the quantity of gait in this population. For

example, the findings of the study of Durcan et al. (2015) suggest that balance self-efficacy

(person’s belief in their ability to undertake activities of daily living without losing their

balance) may be a stronger predictor than physical factors, such as gait speed and balance, in

return to independent community ambulation in chronic stroke patients.25 This is supported

in other studies that describes balance self-efficacy as a factor independently associated with

post-stroke activity and participation in chronic stroke patients26 and has also been found to

be an independent predictor of community reintegration in older adults with chronic stroke.27

A model for community ambulation was developed by Barcley et al. (2015) which shows that

community ambulation after stroke appears to be represented by associations between

ambulation (indoor- and outdoor mobility), gait speed, and health perceptions.28 A

qualitative study concluded that to stimulate outdoor walking activity, it seems important to

influence the intention to walk by addressing social influence, self-efficacy and attitude

towards physical activity in the development of efficient interventions.29 At the same time,


14

improvement of walking ability and creation of opportunity should be considered. So for

outdoor walking and community ambulation after stroke, the incorporation of behavioral,

social, environmental as well as physical variables should be considered.28,29

Study limitations

First, the identification of gait activity for estimating gait parameters was accomplished by a

gait detection algorithm.19Although validity and reliability is good for slow and fast walking, it

still remains unknown to what extent the algorithm identified other forms of cyclic

movements, such as biking. Misclassifications of gait activity or for instance wearing the

accelerometer away from the midline of the lower back will result in deviating estimations of

gait parameters. The median value over the seven measured days of the gait parameters was

used for analysis, to correct as good as possible for this misclassifications.

Second, in this study gait quantity is determined by the number of walking bouts and total

gait activity time. Information about indoor- and outdoor walking were not registered. So this

study describes the quantity of gait of stroke survivors, but can not conclude anything about

participation or community ambulation and is therefore more difficult to compare with other

studies that use these outcome measures.

Bearing these limitations in mind, this study showed the index of harmonicity in VT direction

is significantly related to the number of walking bouts and gait speed is significantly related

to the total gait activity time, after correcting for other variables, in community-dwelling

stroke survivors. The study highlights the need for further research to explain the variance in

quantity of gait in community-dwelling stroke survivors. Next studies should not solely focus

on physical aspects, but should include also the emotional, social, cognitive and

environmental factors that can influence quantity of gait. With more knowledge,

interventions can be more specifically focused to the related factors, whereby the level of

physical activity might be positively influenced.


15

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Appendix 1: Test for multicollinearity

Figure 1: correlation matrix walking bouts


19

Figure 2: correlation matrix gait activity


20

Samenvatting

Doelstelling: Het doel van deze studie is om het verband tussen kwalitatieve loopparameters

en kwantiteit van lopen bij thuiswonende mensen met een CVA te beschrijven.

Methode: In deze cross-sectionele studie worden 55 mensen met een CVA gedurende 7

dagen in hun thuissituatie gemeten met een accelerometer om zo de kwaliteit- en kwantiteit

van het lopen vast te stellen. De afhankelijke variabelen van deze studie zijn de kwantitatieve

loopparameters: het aantal keren dat iemand loopt per 24 uur (‘walking bouts’) en de totale

looptijd per 24 uur. Onafhankelijke variabelen zijn de kwalitatieve loopparameters:

loopsnelheid, schredetijd, loopsymmetrie, de ‘smoothness’ van het lopen en loopvariabiliteit.

Univariate- en multivariate regressie analyses zijn gebruikt om een mogelijke associatie

tussen de kwalitatieve- en kwantitatieve loopparameters vast te stellen.

Resultaten: Op basis van de univariate analyse en na de test op multicollineariteit, zijn zes

variabelen geïncludeerd voor de multivariate analyse met looptijd als afhankelijke variabele.

In deze multivariate regressieanalyse blijft alleen loopsnelheid over in het laatste model, met

een R2 van 0,126. Er zijn vijf variabelen voor de multivariate analyse met het aantal ‘walking

bouts’ als afhankelijke variabele geïncludeerd. In deze multivariate regressieanalyse, blijft de

‘index of harmonicity in VT direction’ over in het laatste model, met een R2 van 0,091.

Conclusie: Deze studie laat zien dat de ‘smoothness’ van het lopen en de loopsnelheid

significant gerelateerd zijn aan kwantiteit van lopen, gecorrigeerd voor andere variabelen.

Deze kwalitatieve loopparameters verklaren maar heel weinig van de variantie in kwantiteit

van lopen, er blijft dus nog veel variantie onverklaard in kwantiteit van lopen. Mogelijk dat

andere factoren, zoals emotionele-, sociale- en omgevingsfactoren meer invloed hebben op

de kwantiteit van lopen in deze populatie.

Klinische relevantie: Interventies kunnen meer gefocust worden op de gerelateerde

kwalitatieve loopparameters (loopsnelheid en ‘smoothness’ van het lopen), waardoor de

fysieke activiteit van deze mensen positief beïnvloed wordt. Daarnaast laat deze studie zien

dat interventies een bredere focus moeten hebben, dan alleen fysieke aspecten.

Kernwoorden: CVA, loopkwantiteit, loopkwaliteit, accelerometrie, fysieke activiteit

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and quantitative gait parameters in community-dwelling stroke survivors