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Development of a model of participation in community-based, discretionary activities

by people who use wheelchairs

Anita Perr

A dissertation submitted to the Graduate Faculty in Environmental Psychology

in partial fulfillment of the requirements for the degree of Doctor of Philosophy,

Graduate School and University Center of The City University of New York

2014

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Anita Perr

iii

This manuscript has been read and accepted for the Graduate Faculty in Psychology in

satisfaction of the Dissertation requirements for the degree of Doctor of Philosophy

___________________ ______________________________

Date Dr. Gary Winkel

Chair of Examining Committee

____________________ ________________________________

Date Dr. Maureen O’Connor

Executive Officer

Dr. John Seley

David Chapin

Dr. David Gray

Dr. Mariette Bates

Abstract

Development of a model of participation in community-based, discretionary activities by

people who use wheelchairs

Anita Perr

Adviser: Professor Gary Winkel

This cross-sectional research analyzed an existing data set of 302 wheelchair users to

identify the psychosocial predictors of participation in community-based, discretionary activities.

Two defining elements of participation were studied: the extent of participation and satisfaction

with participation. Descriptive analyses of the participants’ demographic information and

portions of four assessments were completed first. Regression analyses were then used to

systematically eliminate potential covariates until the significant psychosocial covariates of the

extent of and satisfaction with participation were identified. Perceived control over one’s life and

perceived reintegration to social function were found to predict the extent of participation.

Perceived control also predicted satisfaction with participation as did the participant’s general

mental health. Additionally, because the extent of participation predicted satisfaction, the

perception of reintegration also predicted satisfaction through the extent of participation.

Limitations of this study include those inherent in using an existing data set as well as not

representing wheelchair users from sufficiently diverse racial, ethnic, socio-economic or

geographic backgrounds.

These important findings indicate a need for future study to identify how psychosocial

function is addressed during the physical rehabilitation process and may act as an impetus for

modifications in the education of professionals who work with people with disabilities.

Acknowledgments

There are so many people who supported me through this process. I would like to thank a

few of them here. I send thanks to my workmates, friends, and family for their support and

encouragement through my frustration and learning. I send thanks to my committee David

Chapin and Dr. John Seley and my external readers, Dr. David Gray and Dr. Mariette Bates for

their input and expertise as I planned the research and completed the dissertation defense. I send

thanks to Dr. David Gray and Dr. Holly Hollingsworth for sharing their data and their knowledge

and experience so freely. I send thanks to Dr. Kitch Barnicle for her insight and advice in helping

what was in my head come across in words. And I send thanks to my advisor, Dr. Gary Winkel,

for his mentorship throughout this process and especially for guiding me to ask the right

questions and to find the possible answers.

vii

Table of Contents

Abstract .......................................................................................................................................... iv

Acknowledgments.......................................................................................................................... vi

List of Tables .................................................................................................................................. x

List of Figures .............................................................................................................................. xiii

List of Appendices ........................................................................................................................ xv

Introduction ..................................................................................................................................... 1

Background ................................................................................................................................. 1

Theoretical Rationale .................................................................................................................. 3

Need for the Study ...................................................................................................................... 7

Boundaries of this Research...................................................................................................... 11

Key Terms ................................................................................................................................. 11

Research Questions ................................................................................................................... 14

Summary ................................................................................................................................... 14

Review of the Literature ............................................................................................................... 16

Tools Used to Measure Participation ........................................................................................ 16

Barriers to and Facilitators of Participation .............................................................................. 23

Physical barriers to and facilitators of participation. ............................................................ 23

viii

Psychosocial barriers to and facilitators of participation. ..................................................... 25

Methods......................................................................................................................................... 28

Instruments ................................................................................................................................ 28

The Participation Survey/Mobility (PARTS/M). .................................................................. 29

The Personal Independence Profile (PIP). ............................................................................ 30

The Reintegration to Normal Living Index (RNLI). ............................................................ 31

The Medical Outcomes Study 36-Item Short Form Health Survey (SF-36). ....................... 32

Data ........................................................................................................................................... 33

Participants ................................................................................................................................ 33

Participants contained in the full data set. ............................................................................ 33

Participants in this dissertation study. ................................................................................... 34

Data Analysis ............................................................................................................................ 34

Variables derived from the PARTS/M. ................................................................................ 35

Variable derived from the PIP. ............................................................................................. 37

Variable derived from the RNLI. .......................................................................................... 37

Variables derived from the SF-36. ........................................................................................ 37

Extent of Participation .............................................................................................................. 39

Satisfaction with Participation .................................................................................................. 40

Results ........................................................................................................................................... 41

Participants ................................................................................................................................ 41

Outcome and Explanatory Variables ........................................................................................ 53

Findings Regarding the Extent of Participation in Community-based, Discretionary Activities

by People who Use Wheelchairs............................................................................................... 60

Findings Regarding Satisfaction with Participation in Community-based, Discretionary

Activities by People who Use Wheelchairs .............................................................................. 71

Discussion ..................................................................................................................................... 82

Study Limitations ...................................................................................................................... 89

Using a Secondary Source for Data .......................................................................................... 90

Recommendations for Future Research and Practice ............................................................... 91

Appendix A: UN Convention of the Rights of Persons with Disabilities: Articles Relevant to this

Research ........................................................................................................................................ 95

Appendix B: Letter of Agreement ................................................................................................ 96

Appendix C: The Participation Survey/Mobility(PARTS/M) ...................................................... 97

Appendix D: The Personal Independence Profile (PIP) ............................................................. 111

Appendix E: The Reintegration to Normal Living Index (RNLI) .............................................. 112

Appendix F: The Medical Outcomes Study 36-Item Short Form Health Survey (SF-36) ......... 114

References ................................................................................................................................... 117

List of Tables

Table 1. Structure of the PARTS/M ............................................................................................. 29

Table 2. Potential Covariates ........................................................................................................ 35

Table 3. Continuous Variables: Age, years at present living situation, years since onset of the

disability. ....................................................................................................................................... 42

Table 4. Characteristics of Participants (N=302) .......................................................................... 45

Table 5. Descriptive Statistics Regarding the Outcome and Explanatory Variables .................... 54

Table 6. Scoring Scales of Extent of Participation by Domain ..................................................... 56

Table 7. Extent Factor Analysis Structure Matrix ........................................................................ 56

Table 8. Descriptive Statistics Regarding Extent of Participation in Selected Community-based

Activities of the PARTS/M ........................................................................................................... 57

Table 9. Descriptive Statistics Regarding Importance of Participation ........................................ 58

Table 10. Importance Factor Analysis Structure Matrix .............................................................. 58

Table 11. Descriptive Statistics Regarding Satisfaction with Participation ................................. 59

Table 12. Satisfaction Factor Analysis Component Matrix .......................................................... 59

Table 13. Regression Analysis of Medical and Demographic Covariates of Extent of

Participation .................................................................................................................................. 61

Table 14. Regression Analysis of Covariates of Extent of Participation ...................................... 62

Table 15. Order of Removal and Significance of Non-significant Covariates of Extent of

Participation .................................................................................................................................. 63

Table 16. Extent: Significant Covariate Predictors ....................................................................... 63

Table 17. Extent: Regression Analysis of Covariates and Social Function .................................. 65

Table 18. Extent: Regression Analysis of Covariates and General Mental Health ...................... 65

Table 19. Extent: Regression Analysis of Covariates and Emotional Role Functioning.............. 66

Table 20. Extent: Regression Analysis of Covariates and Vitality ............................................... 66

Table 21. Extent: Regression Analysis of Covariates and Perceived Control .............................. 67

Table 22. Extent: Regression Analysis of Covariates and Perception of Reintegration to Social

Function ........................................................................................................................................ 67

Table 23. Extent: Regression Analysis of Covariates and All of the Psychosocial Factors ......... 69

Table 24. Order of Removal and Significance of Non-significant Covariates of Extent of

Participation .................................................................................................................................. 69

Table 25. Extent: Significant Predictors ....................................................................................... 70

Table 26. Regression Analysis of Medical and Demographic Covariates of Satisfaction with

Participation .................................................................................................................................. 71

Table 27. Regression Analysis of Covariates of Satisfaction with Participation ......................... 72

Table 28. Order of Removal and Significance of Non-significant Covariates of Satisfaction with

Participation .................................................................................................................................. 73

Table 29. Satisfaction: Significant Covariates .............................................................................. 73

Table 30. Satisfaction: Regression Analysis of Covariates and Social Function ......................... 74

Table 31. Satisfaction: Regression Analysis of Covariates and General Mental Health .............. 75

Table 32. Satisfaction: Regression Analysis of Covariates and Emotional Role Functioning ..... 75

Table 33. Satisfaction: Regression Analysis of Covariates and Vitality ...................................... 76

Table 34. Satisfaction: Regression Analysis of Covariates and Perceived Control ..................... 76

Table 35. Satisfaction: Regression Analysis of Covariates and Perception of Reintegration to

Social Function ............................................................................................................................. 77

Table 36. Satisfaction: Regression Analysis of Covariates and All of the Psychosocial Factors 78

xii

Table 37 Order of Removal and Significance of Non-significant Covariates .............................. 78

Table 38. Satisfaction: Significant Predictors ............................................................................... 79

xiii

List of Figures

Figure 1. Sample of SF-36 subscale and item score ..................................................................... 38

Figure 2. Gender ........................................................................................................................... 42

Figure 3. Race ............................................................................................................................... 43

Figure 4. Education ....................................................................................................................... 43

Figure 5. Marital Status................................................................................................................. 44

Figure 6. Income ........................................................................................................................... 44

Figure 7. Primary Diagnosis Leading to Wheelchair Use ............................................................ 47

Figure 8. Incidence of Other Conditions....................................................................................... 49

Figure 9. Frequency of Pain (n=215) ............................................................................................ 49

Figure 10. Frequency of Spasticity (n=155) ................................................................................. 50

Figure 11. Frequency of Skin Problems (n=105) ......................................................................... 51

Figure 12. Frequency of Depression (n=131) ............................................................................... 52

Figure 13. Amount of Assistance Received Per Week ................................................................. 53

Figure 14. A Model Predicting the Extent of Participation by Wheelchair Users in Community-

based, Discretionary Activities. .................................................................................................... 64

Figure 15. A Model Predicting the Extent of Participation by Wheelchair Users in Community-

based, Discretionary Activities. .................................................................................................... 70

Figure 16. A Model Predicting Satisfaction with Participation in Community-based,

Discretionary Activities by Wheelchair Users. ............................................................................. 73

Figure 17. A Model Predicting Satisfaction with Participation in Community-based,

Discretionary Activities by Wheelchair Users. ............................................................................. 79

xiv

Figure 18. A Model of Participation in Community-based, Discretionary Activities. ................. 81

List of Appendices

Appendix A UN Convention on the Rights of Persons with Disabilities ..........................95

Appendix B Letter of Agreement ......................................................................................96

Appendix C Participation Survey/Mobility .......................................................................97

Appendix D Personal Independence Profile ....................................................................111

Appendix E Reintegration to Normal Living Index ........................................................112

Appendix F Medical Outcomes Study 36-Item Short Form Health Survey ....................114

Introduction

Like all people, wheelchair users have a life outside of their homes and work. They have

hobbies and avocational interests, familial and social relations, and needs for inclusion in

activities outside their homes. Participating in these activities is just as important for wheelchair

users as the rest of the population and as such, is considered a right (United Nations General

Assembly, 2006). This dissertation investigated certain aspects of how disability affects social

inclusion. This research used an existing data set to identify the social and psychological

characteristics of wheelchair users that predict participation in community-based, discretionary

activities. Discretionary activities are those that occur by choice, outside of work, chores, and

self-care. My experience as an occupational therapist specializing in seating and wheeled

mobility used by people with disabilities and the existing literature show that clinicians and

researchers focus more on the physical attributes of wheelchair users and their environments than

on the psychological and social attributes associated with being in the community and

participating in discretionary activities. Taking into account physical, environmental, and

demographic contributors, this research examined the social and psychological characteristics of

wheelchair users as predictors of participation in discretionary activities outside home.

Background

According to the 2010 U.S. census data, about 12% of the US civilian, non-

institutionalized population reported a disability, half of whom reported difficulty with their

ability to walk (United States Census Bureau, n.d.). According to the 2005 US Census data,

approximately 3.3 million individuals over 15 years of age, or 1.4% of that population, use a

wheelchair as their primary means of mobility (United States Census Bureau, 2008). It is

expected that the number of people with disabilities and the prevalence of wheelchair use will

increase as baby boomers age (Brault, Hootman, Helmick, Theis, & Armour, 2009; Christensen,

Doblhammer, Rau, & Vaupel, 2009). The vast majority of wheelchair users (at least 93%) report

a limitation in their ability to perform or participate in desired activities (Kaye, Kang, &

LaPlante, 2002). The reasons for the limitations have not yet been thoroughly identified. Until

the causes for the limitations are identified, it is impossible to act upon them and facilitate

improved participation for those who wish to take part in activities in their communities. The

mere numbers of wheelchair users and their perceived limitations due to their disabilities suggest

that further research is needed to identify the psychosocial factors that impede or facilitate

participation in such activities thereby increasing the knowledge base and perhaps suggesting

foci for intervention (Kaye et al., 2002).

The United Nations Convention of the Rights of Persons with Disabilities (the

Convention) recognizes that discrimination against a person on the basis of a disability is a

“violation of the inherent dignity and worth of the person” (United Nations General Assembly,

2006). The Convention is based, in part, on the principle of full and effective participation and

inclusion in society. The Convention was adopted in 2006 and entered into force in 2008 (United

Nations Enable, 2008-2011a) and has 153 signatories (United Nations Enable, 2008-2011b).

Articles 9, 19, 20, 29, and 30 of the Convention clearly act to support the intent of the research in

this dissertation as they directly address accessibility, mobility, and participation in community-

based activities (Appendix A).

It follows then, if access and participation is a right for people with disabilities, it is

necessary to determine how people currently participate in order to determine where

interventions are necessary. It is further necessary to determine the facilitators of and barriers to

participation so that they can be addressed to increase participation where there are limitations.

The issue of participation by people with disabilities is too broad to study as a whole so this

project extracts one specific area to investigate closely.

Theoretical Rationale

There is no single theory or framework of participation. The theoretical base for this

dissertation draws on the work of a number of theorists who describe concepts associated with

participation, specifically participation in community based discretionary activities. Maslow’s

theory uses a hierarchical representation to describe the location of discretionary activities and

social activities taking into account a person’s needs and priorities. According to Maslow’s

hierarchy of needs, people have a need for belongingness and love and a desire for self-esteem

and for recognition, dignity, or appreciation (Maslow, 1987) which can be achieved through

participation in community-based, discretionary activities. At the base of his hierarchy is the

need for food and shelter. These needs to maintain survival precede the need to improve

satisfaction and happiness. It is through meeting needs at basic levels that a person can then

move on to higher levels of existence. Needs at the level of belongingness, a higher level in

Maslow’s hierarchy of needs, may be met in part through participation in social and leisure

activities.

Most of the current research in rehabilitation regarding wheelchair users addresses

function at basic levels, focusing, for example, on mobility and self-care skills which correlate

with Maslow’s two lowest levels, those of physiological needs and the need for safety and

security. There is a lack of research investigating function at higher levels of Maslow’s hierarchy

by people who use wheelchairs users. This dissertation investigates function that occurs at the

levels of love and belonging and self-esteem, both of which are higher levels in Maslow’s

hierarchy.

Oldenburg is another theorist whose work is relevant to this dissertation. He addresses the

need for participation in community based activities when he described what he calls “third

places” (Oldenburg, 1997). In his book, The Great Good Place, Oldenburg describes the roles of

place in the lives of humans. He describes home being a first place and work being a second

place. Related to the research in this dissertation, his description of the important role of informal

public gathering places or “third places” is particularly interesting. Third places are the places

where people go to be a part of their community and to feel comfortable and included

(Oldenburg, 1997). Although not hierarchical, it is interesting to compare Oldenburg’s

discussion of place with Maslow’s hierarchy of needs, relating Oldenburg’s third place with

Maslow’s discussion of the need for belongingness. Although Oldenburg does not address the

needs for people with disabilities to have access to and to feel a part of these third places, he does

describe the need for all people to have these public places for regular, voluntary, and informal

gathering. In my reading of Oldenberg’s work, I include people with disabilities as part of ‘all

people’ although their specific needs and desires may be different than those of other people.

Oldenberg describes the sense of worth that people feel in these third places as a result of being

recognized, accepted, and valued (Oldenburg, 1997). My research begins to investigate whether

and how wheelchair users have places in their lives that act as their third places and may identify

whether third places are important to and available to people who use wheelchairs by

investigating the psychosocial factors that predict participation.

While the research in this dissertation focuses on adults, the following model of

children’s’ participation includes many factors that are relevant to adults and help to support the

work of this dissertation. King, et al. (2003) developed a model of factors affecting the

participation of children with disabilities containing three categories of factors:

1. Factors that reside within the child such as self-perceptions of athletic and scholastic

competence, physical and cognitive function, emotional and social function, and

preferences,

2. Factors that come from the family including supports and preferences, and

3. Factors that reside in the environment, including the presence or absence of barriers

and supportive relationships for the child and the family.

This socio-ecological model addresses the complexities of participation. Factors from three

levels, each containing multiple, variable constructs interrelate in various ways leading to the

complexity of participation. These theorists identify the directions of the relationships between

the different aspects of the model although, by their own admission, the links are based on theory

and logic. Empirical data supporting the direction of the relationships is limited (King et al.,

2003). The model described by King, et al. informs many aspects of participation revealed in the

research of this dissertation.

Nosek and Fuhrer describe a model of independence that defines the contributions to

independence. The elements include perceived control over one’s life, physical function,

psychological self-reliance, and environmental resources (Nosek & Fuhrer, 1992). These

concepts serve as part of the framework of this research.

The International Classification of Functioning, Disability and Health (ICF) is the World

Health Organization’s framework for describing health and health-related states. Participation is

central to the functioning described in the ICF. The ICF defines participation as the nature and

extent of a person’s involvement in a life situation. In a footnote, they go on to state that central

to participation is involvement, taking part, being included, and being accepted. The ICF’s model

of functioning and disability describes the interactions between the person, including his or her

health conditions as well as his or her mental, sensory and motor functions; the activities; and the

environments (Jette, Haley, & Kooyoomjian, 2003; World Health Organization, 2001). The

model defines all of the factors that influence participation and accepts the complex nature of

participation. It accounts for products and technology as well as the natural environment and

human made changes to the environment, as well as support, relationships, attitudes, services,

and systems or policies (Rimmer, 2006). The ICF model is used to describe disability and

function throughout the world and is being used as a foundation for many US and international

programs and services. The ICF has been used as the theoretical base for research on

participation by wheelchair users (Harris, 2007). Harris’ work takes into account issues related to

time, to capacity and actual performance, and to the social and physical environment.

The ICF presents a unified approach to explaining participation by people with

disabilities. In the past, disability theory focused on two separate perspectives: medical and

social. Rather than separating a person into parts, the ICF acknowledges that biological and

societal influences are so intertwined that neither explains participation without the other (Imrie,

2004). Disability is seen as a variation in function due to impairment, activity limitation, and/or

societal participation restrictions. Disability occurs as a result of interactions between an

individual and his or her environment-socio-cultural context. The ICF is flexible enough to

account for differences among people as well as in different environments and societies (Imrie,

2004). This dissertation is based on the ICF model, focusing specifically on the role of a person’s

psychosocial function while accounting for his or her health conditions and while situating the

activities within their environments.

Need for the Study

Many wheelchair users are limited in their participation in activities in their communities.

Barriers to participation include physical factors such as environmental obstacles, weakness, and

poor endurance. Barriers also include societal factors such as limited finances and inadequate

enforcement of laws regarding accessibility, and psychosocial factors such as poor social

functioning and self-efficacy (Cooper, Cooper, McGinley, Fan, & Rosenthal, 2012; R. Kennedy,

2002). Up to this point, little research has addressed the impact of psychosocial functioning on

wheelchair users’ participation in community-based activities focusing instead on the physical

aspects of performing skills and participating in activities. Additionally, little research regarding

wheelchair users addresses activities that are done by choice, in one’s free time focusing instead

on obligatory activities such as self-care and work. This research seeks to develop and evaluate a

model of participation that identifies the psychosocial factors, such as perceived control, that

predict participation in community-based, discretionary activities.

The need for this study was based on three main reasons: 1) limitations in physical

rehabilitation programs and the education of physical rehabilitation professionals, 2) a focus in

research on physical factors relating to wheelchairs and wheelchair use as a proxy for

participation, and 3) the complicated nature of studying and explaining participation. When

people experience a disabling illness or trauma, they frequently undergo physical rehabilitation

in order to return to their desired home- and community-based activities. People born with such

conditions and those who acquire the conditions early in life often undergo repeated courses of

rehabilitation to maximize their abilities to function in various settings including home, school,

workplace, and community. Most rehabilitation programs address the physical factors required to

perform activities like endurance, strength, and wheelchair propulsion techniques but they often

neglect psychosocial factors relevant to community living (M. L. Lund & Lexell, 2008; K. A.

Walker et al., 2010). This neglect of psychosocial functioning may become more acute given our

recent economic crisis and the emphasis on cost containment in healthcare. The focus of physical

rehabilitation is building independence but it is primarily limited to personal self-care issues

while opportunities to address socialization and function within the community are limited.

Wheelchair users participating in rehabilitation programs may be discharged once their basic

needs are met, such as being able to feed or dress themselves, but before more advanced skills

needed for effective social and community function are mastered.

Some researchers report that rehabilitation is shifting somewhat from a biomechanical

approach to a more holistic, client-centered approach which expands the opportunities to address

psychosocial functioning in physical rehabilitation settings (Cardol, De Jong, & Ward, 2002). In

a client-centered approach, the patient identifies his or her needs and participates in developing

his or her program of rehabilitation. While this may be the case, I contend that psychosocial

functioning is not addressed sufficiently by rehabilitation practitioners. While the client has

input, it is still within the confines of institutional and funding policies which focus on basic,

home-based, self-care skills. Because psychosocial functioning is not a priority during physical

rehabilitation, people undergoing rehabilitation may not achieve their desired levels of

independence or community reintegration. In order to integrate psychosocial functioning into

physical rehabilitation, it is imperative to describe the roles that psychosocial factors play in

predicting participation. Once the predictors are identified and a comprehensive model of desired

community engagement is described, researchers will be able to focus their attention on

strategies to incorporate an emphasis on psychosocial functioning during physical rehabilitation.

It may be possible to increase the efficiency and effectiveness of rehabilitation programs and

expedite the person’s return to community life. The content of educational programs for

rehabilitation professionals such as occupational and physical therapists who work with

wheelchair users in an ongoing manner may also need to be modified to emphasize

psychological and social functioning.

Participation in community-based activities varies greatly among wheelchair users. In

looking at popular media, there are wheelchair users who are quite active and visible in everyday

life. Examples of this variation include the popularity of sporting events and television shows

including participants who use wheelchairs. Conversely, isolation of and barriers to participation

are also evident in today’s culture. For instance, the media often depict wheelchair users as being

alone or in need of help. Participation in community based activities varies greatly among

wheelchair users and as yet is not predictable. A review of the extant literature does not clarify

whether or why some wheelchair users participate to a greater extent than others and it does not

emphasize the psychosocial factors that predict community-based participation. Participation is a

complicated concept that is likely affected by a wide variety of personal and societal factors

(Bode, Hahn, Bernspang, & Lexell, 2010). Most research on participation by wheelchair users

has focused on physical factors like propulsion speed, pushrim style, and medical diagnosis

(Chow & Levy, 2011; Dieruf, Ewer, & Boninger, 2008; Giesbrecht, Ripat, Quanbury, & Cooper,

2009; Harris & Sprigle, 2008; Howarth, Pronovost, Polgar, Dickerson, & Callaghan, 2010). To

use concepts such as these as markers of participation is shortsighted because the physical act of

propelling a wheelchair does not equate with participation. Further research must be done to

explore the relationships between the physical aspects of wheelchair mobility and the functional,

social, and psychological aspects of participation in selected activities. A recent study in the

Netherlands revealed that while there was a relationship between the wheelchair user’s

satisfaction with the fit and dimensions of his or her wheelchair with a more active lifestyle,

there was not a significant relationship between wheelchair-related characteristics and overall

participation as measured using the Dutch version of the Quebec User Evaluation of Satisfaction

with Assistive Technology (de Groot, Post, Bongers-Janssen, Bloemen-Vrencken, & van der

Woude, 2011).

Research that involves the psychosocial factors predictive of participation has primarily

focused on quality of life and satisfaction (Boschen, Tonack, & Gargaro, 2003; M. P. Dijkers,

1999; M.P. Dijkers, 1999; Tate, Kalpakjian, & Forchheimer, 2002). While quality of life and

satisfaction may result from participation, there is insufficient evidence regarding the

relationship between participation in discretionary community-based activities and quality of life

or satisfaction or that quality of life and satisfaction predict participation. Most participation

studies focus on a conglomeration of activities including 1) those that occur in the home, 2) those

that occur in the community, 3) those that are mandatory (such as bathing, dressing, grooming,

toileting, and work), and 4) those that are discretionary (performed by choice, after mandatory

activities are completed) (Boschen et al., 2003; Sonenblum, Sprigle, Harris, & Maurer, 2008).

Much of this research does not focus specifically on wheelchair users; instead it includes wide

variation in means and ability of mobility. While this research may include too broad a

population to be directly useful to any individual, it is important because it describes the

complexities of participation and offers a basic theoretical framework that can be used as a

starting point for describing participation among specific populations. Participants in these

studies were often grouped in ways that do not differentiate whether they use wheelchairs. For

instance, some researchers looked at people with all types of mobility impairments including

those who use walking aids like canes and walkers or otherwise have difficulty walking (M. L.

Lund, Nordlund, Bernspang, & Lexell, 2007). Others have grouped participants by diagnostic

category rather than by functional ability (P. Kennedy, Lude, & Taylor, 2006; M. L. Lund,

Nordlund, Nygard, & Bernspang, 2005; Noreau & Fougeyrollas, 2000). By studying these mixed

groups and by including a wide variety of activities in the research, it is impossible to describe

wheelchair users’ desired community engagement. My research focuses specifically on

wheelchair users and on community-based, discretionary activities in an effort to fill this gap in

the literature.

Boundaries of this Research

This research focused on adults who use wheelchairs for the majority of those activities

requiring mobility outside their home. This research addressed men and women, aged 18 and

over, living in the community in rural, suburban, or urban settings. It addresses participation in

community-based activities only. Participation in activities performed in the home were not

considered as part of this research. This research addressed only discretionary activities and did

not include those activities which the participants were compelled or required to perform like

work or daily self-care tasks.

Key Terms

Some of the concepts that are central to this project have multiple meanings in common

language that differ from their usage in disability literature. It is therefore necessary to explain

the specific use of some of the concepts in the context of this research. For the purpose of this

research, the term wheelchair is any wheeled mobility device such as a manual or power

wheelchair or a scooter. For the purpose of this research, a wheelchair user is a person who uses

his or her wheelchair for community access. Participation has long been difficult to define and

measure in the field of physical disabilities rehabilitation. For the purpose of this study,

participation refers to the active or passive engagement in an activity. Participation may occur

alone or with other people. It may involve physical and/or cognitive engagement. Participation

involves personal choice and individual meaning (Hammel et al., 2008). Community-based,

discretionary activities are those activities in which the person participates by choice, for their

own sake or pleasure, rather than from mandate or obligation. My research focuses on activities

that occur outside of the home, in public or private locations, indoors or outdoors and as such,

are labeled as community-based. Such activities include active recreation like playing basketball

or camping; leisure activities like attending movies or reading; taking vacations; socializing;

religious activities like attending weekly services or singing in a choir; and community activities

like attending community meetings or serving on a community board (Gray, Hollingsworth,

Stark, & Morgan, 2006).

This research focuses specifically on the role of psychosocial functioning on desired

community engagement. Psychosocial factors are the psychological and social characteristics

that are thought to influence a person’s participation in activities or make some people more or

less likely than other people to do what they want to do. In the literature in this field and in the

instruments used to measure such concepts, the psychosocial factors include the following:

vitality, social function, emotional role function, mental health, perceived control over one’s life,

and perceived satisfaction with the performance of everyday activities.

Vitality refers to a person’s energy or fatigue level. Social function refers to the type and

frequency of participation in social activities and considers the impact that physical health and

emotional problems have on it. Emotional role functioning refers to a person’s ability to function

in his or her usual roles, like worker or caregiver, and considers the influence of mental health on

role performance. Emotional role functioning accounts for limitations that occur as a result of

personal and emotional problems. Mental health is an umbrella term that includes anxiety,

depression, loss of behavioral or emotional control, and psychological well-being (Ware &

Sherbourne, 1992).

Perceived control over one’s life refers to the sense that what happens in one’s life is as a

result of the person’s own actions. It relates to the feeling of power to direct one’s life and the

ability to make choices (Nosek, Fuhrer, & Howland, 1992). Perceived satisfaction with the

performance of everyday activities refers to an individual’s perception of his or her physical,

psychological, and social characteristics that affect performance of routine living patterns (SL

Wood-Dauphinee, Opzoomer, Williams, Marchand, & Spitzer, 1988). Perceived satisfaction

with the performance of everyday activities is a predictor of quality of life.

In addition to psychosocial factors, sociodemographic and medical characteristics also

play a role in a person’s participation. Factors considered as central to this study are wheelchair

type, age, gender, race/ethnicity, marital status, education, income, living arrangement and

location, primary condition causing mobility impairment, time since onset of condition, other

medical conditions impacting participation, amount and type of help needed, and transportation

methods used.

Research Questions

My research used an existing data set to describe a model of participation that identifies the

predictors of a wheelchair user’s participation in community-based, discretionary activities. The

data set includes information about the participants’ impressions of their physical and

psychosocial functioning during various activities in various environments. My research

investigated the roles of the following psychosocial factors as they predict participation in

community-based, discretionary activities: vitality, social function, emotional role function,

mental health, perceived control over one’s life, and perception of reintegration to social

activities.

1. After controlling for demographic and medical covariates, to what extent do each of the

psychosocial factors of interest predict the extent of participation in community-based,

discretionary activities by wheelchair users?

2. After controlling for the level of perceived importance, to what extent do each of the

psychosocial factors of interest predict participation in each of the domains of interest of

community-based, discretionary activities by wheelchair users?

3. After controlling for the extent and level of perceived importance, to what extent do each

of the psychosocial factors of interest predict satisfaction with participation in each of the

domains of interest of community-based, discretionary activities by wheelchair users?

Summary

This study will use an existing data base to describe a model of participation by

wheelchair users in community-based, discretionary activities. Participation research tends to

focus 1) on larger populations than wheelchair users, including people with all disabilities, with

or without mobility limitations; 2) on self-care, leisure, and work activities as a whole; and 3) on

activities inside and outside of the home. Results of those studies cannot be easily generalized to

wheelchair users nor can they be generalized to specific types of activities or specific locations.

Wheelchair users might have unique concerns or needs related to participation in community-

based, discretionary activities. Additionally, prior research focused on physical and

environmental factors that predict participation. Intrinsic, psychosocial characteristics are also

likely to predict participation in community based, discretionary activities. This research fills a

gap in explaining community-based participation by wheelchair users’ by identifying and

describing the psychosocial factors that predict participation.

Review of the Literature

This dissertation investigates certain aspects of participation by people who use

wheelchairs in community-based activities. The literature search revealed little literature

specifically on this topic. This literature review broadens the topic slightly to comprise two

topics that are integral to the investigation: 1) measurement tools and 2) barriers to and

facilitators of participation.

Tools Used to Measure Participation

My review of the tools designed to measure participation in community-based activities

revealed that there is no single, comprehensive instrument that addresses all of the factors related

to participation. Instead many researchers have begun to develop various tools to measure

participation (Boschen et al., 2003; Brown et al., 2004b; Cardol, Beelen, et al., 2002; M.P.

Dijkers, 1999; Fougeyrollas, 2010; Kannisto, Merikanto, Alaranta, Hokkanen, & Sintonen, 1998;

Noreau & Fougeyrollas, 2000; van Brakel et al., 2006). While some of the tools have been

embraced by researchers and clinicians none has been identified as the most effective in the field

of rehabilitation. This section of the literature review mentions these tools and then focuses on

the Participation Survey/Mobility (PARTS/M; Appendix C) from which the dependent variables

in this study were derived. The complicated nature of describing and measuring participation is

partly due to whether measures should be subjective, objective, or both, and from whose

perspective the measures should be made (Brown et al., 2004a; Coster & Khetani, 2008). In an

effort to build the knowledge base in participation and rehabilitation, there has been a push to

quantify constructs that are subjective in nature. Participation involves the interplay between the

person and his or her environment involving choice, control, and opportunity and is interpreted

within each individual’s personal and social values (Hammel et al., 2008).The instruments that

are used most frequently in research on participation by people with physical disabilities are the

Craig Handicap Assessment and Reporting Technique (CHART) (Whiteneck et al., 1992), the

Canadian Occupational Performance Measure (COPM) (Law et al., 1994), the Functioning

Everyday with a Wheelchair Instrument (FEW) (Holm, Mills, Schmeler, & Trefler, n.d.), and the

Participation Survey/Mobility (PARTS/M) (Gray et al., 2006).

The CHART measures performance in six domains: physical and cognitive

independence, mobility, occupation, social integration, and economic self-sufficiency (Hall,

Dijkers, Whiteneck, Brooks, & Krause, 1998; Whiteneck et al., 1992). It does not however,

include participation in religious or political activities. It was found to differentiate levels of

function consistently with the Functional Independence Measure, a widely used measure of

burden of care (N. Walker, Mellick, Brooks, & Whiteneck, 2003).

The COPM uses a semi-structured interview to identify areas of functioning with which a

person has concerns or difficulties. After identifying the problems or concerns, the person is

asked to rate their importance and their satisfaction with that activity (Law et al., 2000; Law et

al., 1994). Two areas of the COPM, productivity that includes paid/unpaid work and school work

and leisure including quiet leisure, recreation, and socialization, may be helpful in describing a

person’s participation.

The FEW is a questionnaire that measures the wheelchair user’s perceived ability to

function in ten areas (Holm et al., n.d.; Mills, Holm, & Schmeler, 2007). Although considered a

participation measure, the FEW measures the person’s capacity, or potential ability, to

participate. It includes measures of indoor and outdoor mobility and transportation.

The current study uses The Participation Survey/Mobility (PARTS/M) which was

developed by Dr. David Gray as part of the Research and Training Center on Measurement and

Interdependence in Community Living in St. Louis, Missouri

(https://enablemob.wustl.edu/CommunityParticipation.html). The PARTS/M contains 120

survey items in 20 life activities areas that are grouped into six life domains including self-care;

mobility; domestic life; interpersonal interactions and relationships; major life areas; and

community, social, and civic life. Each life domain addresses four components of participation:

temporal (such as amount of time), evaluative (such as choice and satisfaction), relation to health

(such as fatigue or pain), and supportive (such as assistance required).

The PARTS/M has been used to investigate the relationship between participation and

physical activity levels, the need for support, wheelchairs, and speeds of travel. The studies

reviewed below used specific aspects of the PARTS/M but did not look specifically at

participation in activities in the community nor did they specifically identify the psychological

factors that predict greater participation.

In one study, the PARTS/M was used along with wheelchair data loggers to investigate

the correlation between mobility characteristics like distance traveled, speed, number of stops

made, and the frequency of participation (Cooper, Ferretti, Oyster, Kelleher, & Cooper, 2011).

The data loggers were electronic sensors that were attached to the participants’ wheelchairs by

the research personnel in such a way that they did not interfere with regular use of the wheelchair

during everyday activities. They were used to calculate and record the speed, distance travelled,

number of stops, and the amount of time the wheelchair was used. The participants were asked to

conduct their daily activities as usual. Data were gathered over a three week period; the first

week was during the 2007 annual National Veterans Wheelchair Games. The second and third

weeks occurred when the participants were in their home environments. Data collected during

the two weeks in the home environment were used for this study. In addition to the data logger,

participants also completed the PARTS/M. Of the 31 participants recruited, 22 completed all

portions of the study. Because data from eight of those 22 participants could not be used either

because of instrumentation problems, participant’s inability to use their wheelchairs during the

study timeframe, or incomplete PARTS/M data, data from 14 participants were used for analysis

to compare their mobility characteristics with their PARTS/M community participation scores.

Data were analyzed for the group of 14 wheelchair users as a whole and for manual

wheelchair users and power wheelchair users as separate groups. Twenty four correlations

yielded two significant findings. The researchers found a significant positive correlation among

manual wheelchair users between the average speed traveled and the frequency of using

transportation (rs = .837, p = .019) and socialization (rs = .772, p = .042). Manual wheelchair

users who wheeled their wheelchairs faster also used transportation more frequently and reported

higher levels of socialization. The researchers also identified a trend towards a significant

correlation between the average speed and the total community participation score among

manual wheelchair users (rs = .714, p = .071). Among power wheelchair users, they found a

trend toward a significant negative correlation between average speed and leisure participation

(rs = -.635, p = .066).

I question the usefulness of these findings for a number of reasons. Firstly, the

participant pool was very small and homogenous. There were only 14 participants included in

the correlation analysis. Although 14 participants were included in the data analysis, the authors

report the demographics on the 16 participants who completed all aspects of the research

including the two whose PARTS/M surveys were incomplete. Of those 16 people, 15 were men

and 12 were white, representing an homogeneous group. Additionally, they were recruited

during a veteran’s athletic event and this event might attract a certain type of person. The

findings may not reflect performance of a larger sample of people with more varied experiences,

histories, and interests. Secondly, by running so many correlations, the researchers increased the

likelihood of finding significance simply by chance. Lastly, the conceptual link between the

variables studied and participation is questionable, even if significance was found. Traveling

further or faster, making more or less frequent stops, and spending more time driving may not

necessarily relate at all to participation. Wheelchair propulsion speed, for example, could be a

function of the person’s physical stamina or whether the environment is spacious, obstacle-free,

and smooth-surfaced.

Another group studied the relationship between the wheelchair, the person’s impairment,

and the environment in three settings: at home, in the community, and during transportation

(Chaves et al., 2004). Seventy spinal cord injured wheelchair users in Pittsburgh and St. Louis

were assessed using three sections of the PARTS/M: getting around inside the home, leaving the

home, and transportation. The participants’ responses were divided into two categories: 1)

participation limitations or health-related factors leading to limitations and 2) access limitations,

or non-health related factors like the physical environment, wheelchair, social attitudes, self-

concept, and lack of assistance. The frequencies of perceived reasons for limitations were used to

calculate the percentage that each factor was perceived to be a limitation in each of the three

settings. Chi-square tests were used to analyze differences between those with paraplegia and

tetraplegia and between the group in Pittsburgh and the group in St. Louis. A significantly higher

percentage of participants in St. Louis indicated that wheelchair seating was a limiting factor for

leaving the home than participants in Pittsburgh (St. Louis, 24%; Pittsburgh, 5%, p = .025).

Participants in St. Louis also reported that social attitudes (St. Louis, 18%; Pittsburgh 0%, p =

.007) and self-concept (St. Louis, 15%, Pittsburgh 0%, p = .015) were limiting factors for leaving

the home at significantly higher rates than participants in Pittsburgh. Lastly, people in St. Louis

reported that social attitudes were limiting factors for transportation at significantly higher rates

than participants in Pittsburgh (St. Louis, 15%, Pittsburgh, 0%, p = .017) (Chaves et al., 2004).

While these researchers found significance in the frequency of the wheelchair being a limiting

factor, the social and psychological findings are also very interesting and should be examined

further. However, the researchers did not define the terms social attitudes or self-concept nor did

they describe how these concepts were measured. This interests me because it is not overtly

obvious how these data were extracted from the PARTS/M. In addition to these findings

regarding social attitudes and self-concept, it would be interesting to investigate the participants

in St. Louis and Pittsburgh further to determine their differences in perception. It would be

important to determine whether there are differences in society’s perception and acceptance of

disability among people living in these locations.

A third group of researchers used the PARTS/M, SF-36, and RNLI to study the

relationship between physical activity and participation in major life activities of a group of 604

people with mobility impairments (Crawford, Hollingsworth, Morgan, & Gray, 2008). They used

the US Department of Health and Human Services Centers for Disease Control and Prevention

criteria for physical activity to identify three categories of activity level: high active- either 30

minutes of moderately intense activity five days weekly or 20 minutes of vigorous activity three

days weekly; insufficient physical activity- more than 10 total minutes per week of moderate or

vigorous activity but less than the recommended amount; and inactivity- less than 10 minutes

per week of moderate or vigorous activity (Crawford et al., 2008). Assignment to the groups

was based on responses to questions on the PARTS/M regarding the frequency and duration of

exercise inside and outside the home and participation in active recreation outside the home.

Using an ANOVA to examine the relationship between activity level and participation, they

found that the frequency of participation in the mobility, community, social, and civic life

domains of the PARTS/M was significantly higher for the high active group than the two lower

activity level groups. They also found that satisfaction and choice were significantly higher in

the high active group for self-care (p<.05), mobility (p<.01), domestic life (p<.01), and

community, social, and civic life (p<.01). Using an ANOVA to examine the relationship between

activity level and health status from the SF-36 scores, the inactive group was found to be

significantly lower on the social functioning subscale than the high active group (p<.01).

Additionally, the inactive group rated themselves significantly lower than the low active and

high active groups for the vitality subscale (p<.01) and for pain (low active group, p<.05, high

active group, p<.01). Using the RNLI to compare activity with reintegration to normal living,

they found that the high active group took more trips out of town and were more satisfied with

their personal assistance for self-care than the inactive group (p<.01). They also found that the

low active group reported higher frequency of enjoyable work than the inactive group (p<.05)

and that the high active group scored significantly higher than the low active group on

recreational activities (p<.05) and significantly higher in social activities than the inactive group

(p< .01). They found that the low active group scored significantly higher in social activities than

the inactive group (p<.01) (Crawford et al., 2008). The findings of this study are important and

support the need for further investigation. My research complements Crawford’s work but differs

from it in some significant ways. While Crawford focused on engagement through physical

activity, I am interested in looking at engagement in a broader sense by looking at participation

in all discretionary activities. Crawford investigated physical activity whether it took place in the

home or outside of the home. I am especially interested in focusing on activities that take place

outside of the home. Lastly, Crawford’s research focused on a group of people with all types of

mobility impairments including those who were able ambulate without a wheelchair. Using the

same data set, my research focuses specifically on wheelchair users.

These studies exemplify the potential usefulness of the PARTS/M in measuring

participation in activities in the community among wheelchair users. While these findings are

interesting, they do not fully explain participation. For instance, they do not address the

importance of considering how peoples’ experiences or how long they used their wheelchair

(time since onset) might affect their participation. They do not elaborate on the differences or

similarities between power and manual wheelchair users. My study will use multiple instruments

to develop of model of participation in community-based activities among wheelchair users.

Barriers to and Facilitators of Participation

Physical barriers to and facilitators of participation.

Much of the literature on wheelchair users’ participation focuses on physical barriers and

facilitators including environmental access, personal propulsion skills, and wheelchair features.

Participation requires that a wheelchair user be able to maneuver a wheelchair successfully in a

variety of environments. The American National Standard Index (ANSI) and International

Standard Organization (ISO) identify testing and reporting procedures for the performance

characteristics of wheelchairs such as tipping angles, turning abilities, obstacle climbing abilities,

and strength/durability of parts and whole systems. Based on these and other procedures, there

has been some research that informs everyday wheelchair use. For instance, in a study of power

and manual wheelchairs, Koontz and colleagues (2010) determined the styles and configurations

of the most maneuverable wheelchair systems by testing maneuvers like 360˚ and U-turns

(Koontz, Brindle, Kankipati, Feathers, & Cooper, 2010).

Driving characteristics like speed and distance traveled have been used as indicators for

participation (Bussmann et al., 2010; Harris, Sprigle, Sonenblum, & Maurer, 2010; Sonenblum et

al., 2008). Another group of researchers studied the participation of veterans in two locations: at

the National Veteran’s Wheelchair Games and in their own communities (Tolerico et al., 2007).

The researchers reported that the veterans traveled farther and faster during the athletic events

than they did in their own communities. They concluded that speed and distance traveled

correlated with participation but they did not say how or why speed and distance specifically

related to levels of participation. With the limited research in this area, it is not possible to draw

the conclusion they reported. They did not consider the contexts within which the participants

were acting. In the context they studied, speed and distance traveled made sense. In other words,

the size of the arena and distance between events may have dictated the distances traveled. In

another context, speed and distance may not have been important indicators of participation.

Wheelchair equipment itself can also be a barrier or facilitator to participation as

participation may be greater when the person is able to use his or her wheelchair to its fullest

advantage. Certain aspects of wheelchairs have been studied and have led to the design of new

styles of wheelchairs and of components that may impact function. Two recent and most notable

design changes are related to 1) the handrim shape and placement on manual wheelchairs and to

2) power assist systems that increase the effectiveness of manual propulsion (Dieruf et al., 2008;

Giesbrecht et al., 2009). The newly designed pushrims are shaped to match the user’s hands and

to increase efficiency of pushing. The power assist systems increase efficiency in that each push

of the pushrim is given a boost, making the wheelchair roll further than an unassisted push. The

researchers concluded that equipment such as these make propulsion easier and therefore leads to

increased participation. Their views are short-sighted, however, because their research focused

only on those specific wheelchair parts and did not investigate the combined effect of the other

many physical, societal, and psychosocial factors that influence a person’s participation.

Psychosocial barriers to and facilitators of participation.

Psychosocial barriers to community participation include issues such as the wheelchair

user’s confidence and sense of self-efficacy and his or her impressions of the psychological and

social attitudes of other people in the environment. It seems clear that the psychosocial context

influences participation at least as powerfully as the physical environment. Psychological factors

such as self-consciousness and the attitudes of non-disabled people, including professionals,

were also identified as barriers (Rimmer, Riley, Wang, Rauworth, & Jurkowski, 2004). For

example, in a study of how people with disabilities use gyms, Buffart (2009) found that the staff

members’ limited knowledge of how people with disabilities could use the gym equipment was a

barrier to participation. Another group of researchers found that attitudinal barriers such as

negative staff attitudes were barriers to participation in leisure travel by people with disabilities

(Card, Cole, & Humphrey, 2006). Conversely, attitudes and support from family, friends, and

colleagues can facilitate participation (Noreau, Fougeyrollas, & Boschen, 2002).

While there is a body of research that relates to psychosocial aspects of physical

disability, it does not specifically focus on wheelchair users nor does it specifically address

participation in community-based, discretionary activities. The studies vary greatly. When

looked at as a group, they do seem to indicate the importance of investigating the role of

psychosocial functioning on participation by people with physical disabilities. Persson and

Ryden interviewed 26 people with physical disabilities regarding their coping strategies (Persson

& Ryden, 2006). They found that most of the interviewees acknowledged the importance of

developing confidence or trust in themselves. This relates to a person’s ability to make choices

and the importance of having choice in their own lives. Ozanne, Strang, and Persson studied the

health-related quality of life, anxiety, and depression of people diagnosed with amyotrophic

lateral sclerosis (ALS) and their closest relatives (Ozanne, Strang, & Persson, 2011). They found

higher incidences of anxiety and depression as well as a lower health-related quality of life

indicators in these research subjects than in the general population. Their study did not however,

investigate whether there was a relation between the levels of anxiety or depression and the

subjects’ patterns of participation in discretionary activities in their communities. In a study of

hopefulness, depression, and participation among people who had a stroke, researchers found

that hopefulness and participation may predict depression among stroke survivors (P.D.A. Gum,

CR Snyder, & P.W. Duncan, 2006).

A number of studies have been performed to determine whether there is a link between

physical activity and participation (Bergland & Narum, 2007; R.W. Motl & E. McAuley, 2010).

In an invited clinical commentary for the New Zealand Journal of Physiotherapy, Mulligan

described numerous studies that indicate that the physiotherapist (rehabilitation professional) can

positively influence the patterns of participation by people with long term disabilities which in

turn, positively influences overall health and well-being (Mulligan, 2011). The studies that

Mulligan describes also indicate the importance of social relationships and psychosocial

functioning like self-efficacy in improving well-being. In a study of people with multiple

sclerosis, 292 participants were examined to explain the relationship between psychosocial

functioning, physical activity, and quality of life. The researchers found that the relationship

between physical activity and quality of life is likely indirect and that psychosocial factors like

mood, pain, social support, and self-efficacy (among other factors) are intermediate variables

(Motl, McAuley, Snook, & Gliottoni, 2009). Although this study investigates a different

population, it clearly identifies the role of psychosocial functioning on activity and supports the

need for further related research.

The literature described here points out the areas that have been emphasized thus far in

explaining participation by people with disabilities. The research focusing specifically on

participation by wheelchair users focuses primarily on the physical aspects of disability and on

the equipment used by disabled people. While there is some literature that also includes the role

of psychosocial functioning on participation, this pool of literature is limited. It was necessary to

broaden the scope of the literature review to a wider population of people with disabilities to find

more research explaining the role of psychosocial functioning on participation. This supports the

need for further investigation of wheelchair users in order to develop a comprehensive model of

participation among wheelchair users in discretionary, community-based activities and leads

directly to the following research questions.

Methods

This research was conducted using an existing data set provided by Dr. David Gray, Ph.

D., Associate Professor of Neurology and Occupational Therapy at Washington University

School of Medicine in St. Louis, MO (Appendix B). Gray’s research interests encompass broadly

ranging disability and socio-political topics including accessibility, care provision, and equality.

The data were collected with support from the National Institute on Disability and Rehabilitation

Research (NIDRR), award number H133B060018 as part of the Research and Training Center on

Independent Living at The University of Kansas, Lawrence, KS. This NIDRR funded project

studied how people with mobility impairments perform skills within and outside of their homes.

Data were collected regarding environmental access and participants’ opinions regarding their

own functional abilities. These data were also used to determine the parametric characteristics of

the PARTS/M, a measure developed by Gray with funding from the Centers for Disease Control

and Prevention (R04/CCR714134-03S).

Instruments

The data extracted and analyzed in this dissertation were collected using the following

four instruments: The Participation Survey/Mobility (PARTS/M; Appendix C), The Personal

Independence Profile (PIP; Appendix D), The Reintegration to Normal Living Index (RNLI;

Appendix E), and the Medical Outcomes Study 36-Item Short Form Health Survey (SF-36;

Appendix F).

The Participation Survey/Mobility (PARTS/M).

The PARTS/M is a self-report survey developed by Gray (2006), which focuses on 20

major life activities. The individual items that make up the PARTS/M instrument generate

responses that are categorized into six domains (Table 1). In addition to the six domains, the

instrument measures four components of participation. These components of participation cut

across all six domains. The four components are 1) temporal -- frequency and time; 2) evaluative

-- choice, satisfaction, and importance; 3) health-related --limitations as a result of illness, pain,

or fatigue; and 4) supportive -- is assistance needed from another person or from

accommodations, adaptations, or special equipment.

Table 1. Structure of the PARTS/M

Domain Activities

Self-care Dressing

Bathing

Bladder care

Bowel care

Meals

Mobility Move inside home

Leave home

Vacations

Domestic life Work inside home

Exterior maintenance

Interpersonal interactions and

relationships

Parenting

Intimacy

Major life areas Employment

Volunteering

Money management

Community, social, and civic life Active recreation

Leisure activities

Socializing

Community activities

Religious activities

The PARTS/M uses a combination of nominal and ordinal scales. An individual’s overall

PARTS/M score can be calculated, as can scores for each of the 6 domains, each of the 4

components, or each of the 20 life activities (Crawford et al., 2008). The internal consistency of

the two domains of interest in this dissertation study, as calculated using Cronbach’s Alpha, were

mobility = .72 and community, social, and civic life = .85. The internal consistency of the three

variables derived from the evaluative component of the PARTS/M as calculated using Cronbach

alphas were satisfactory: choice items = .93, satisfaction items = .94, and importance items =

.78) (Gray et al., 2006).

This dissertation used data collected from those portions of the PARTS/M related to

participation in discretionary, community-based activities. Of the 20 activities in the PARTS/M,

this study focused on the following six areas: active recreation, leisure activities, vacationing,

socializing, religious activities, and community activities. These areas are contained within two

domains: mobility and community/social/civic life. The three variables derived from the

PARTS/M were: 1) extent of participation in selected community-based activities (extent), 2)

importance of participation in selected community-based activities (importance), and 3)

satisfaction with participation in selected community-based activities (satisfaction).

The Personal Independence Profile (PIP).

The PIP is a self-report measure of psychological and environmental aspects of independence

based on a model of independence emphasizing control of life, having options, making decisions,

performing daily activities, and participating in community life (Bolton, 2001). The PIP is

comprised of three subscales: perceived control over one’s self, psychological self-reliance, and

physical functioning. Perceived control over one’s self is measured with 10 questions using a 5-

point Likert scale ranging from 1= no control to 5= complete control. It is scored by summing

the responses for a total possible score of 50 with a higher score indicating a higher level of

perceived control. Internal consistency using Cronbach’s α is .86, demonstrating high reliability.

The Psychological Self-reliance subscale measures the character traits of confidence,

assertiveness, and ambition using a 34 item, 5-point Likert scale ranging from 1= completely

agree to 5= completely disagree. Items in this Psychological Self-reliance subscale are from

Fordyce’s Independence Scale (Fordyce, 1953). Cronbach’s α for the Psychological Self-reliance

subscale is .79.

The Physical Functioning subscale contains 25 items that assess the degree of independence

from other people in performing daily activities. Cronbach’s α is .93 for the physical functioning

subscale (Nosek et al., 1992).

The Reintegration to Normal Living Index (RNLI).

The perception of reintegration to social activities was measured using the Reintegration to

Normal Living (RNLI) scale, an 11-item self-assessment developed to determine an individual’s

perception of and satisfaction with his or her level of reintegration into social activities.

Reintegration to Normal Living is described as “reorganization of physical, psychological, and

social characteristics of an individual into a harmonious whole so that one can resume well-

adjusted living after an incapacitating illness or trauma” (SL Wood-Dauphinee et al., 1988).

Scoring of each item ranges from 0 = no integration to 10 = complete reintegration on a visual

analogue scale. The adjusted score ranges from 0 -100 with a higher score indicating better

perceived integration. Cronbach’s α was greater than .9 when tested with 109 patients with

cancer, cardiac conditions, center nervous system disorders, and orthopedic conditions, and their

families/significant others and health professionals (S. Wood-Dauphinee & Williams, 1987). In a

study on well-being in older community residents, Steiner, et al. report the Cronbach’s α as .83

in their primary sample of 414 participants and .76 in their reliability sample of 50 participants

(Steiner et al., 1996).

The Medical Outcomes Study 36-Item Short Form Health Survey (SF-36).

The SF-36 is a 36-item self-report measure of health-related quality of life. Four variables are

derived from data collected with this instrument: vitality, social function, general mental health,

and emotional role function. The SF-36, one of the most widely used health scales, is organized

into two major dimensions: physical and mental health. It contains eight scales: Physical

Functioning, Role Physical, Bodily Pain, General Health Perceptions, Vitality, Social

Functioning, Role Emotional, and Mental Health (McHorney, Ware, Lu, & Sherbourne, 1994).

The Physical Functioning scale measures the person’s involvement in various everyday activities

including exercise, walking, and bending. The Role Physical scale measures accomplishments

and limitations in the ability to work or perform other activities. The Bodily Pain scale measures

both the intensity of pain and the extent to which it limits participation with usual activities. The

General Health Perception scale measures the extent to which the person sees him or herself as

healthy or ill as well as his or her expectation of health in the future. The Vitality scale measures

the person’s perception of his or her energy level. The Social Functioning scale includes items

related to health problems that interfere with social activities. The Role Emotional scale

measures the extent to which the person sees him or herself as accomplishing less than desired.

The Mental Health scale measures emotions like nervousness, sadness, calmness, and

downheartedness (McHorney, War Jr, Lu, & Sherbourne, 1994). The SF-36 is widely used with

various populations, has been translated into 140 languages, and is available in numerous

formats including fixed form, oral interview, online, fax, eForm, smartphone, tablet/kiosk, and

interactive voice response via telephone (McHorney, Ware, et al., 1994) (McHorney, War Jr, et

al., 1994; McHorney, Ware, et al., 1994; Quality Metric, 2012; StrokEngine, n.d.). Reliability of

the SF-36 (Cronbach’s α) ranges from .78 to.93 as follows: physical functioning = .93, role

physical = .84, bodily pain = .82, general health = .78, vitality = .87, social functioning = .85,

role emotional = .83, mental health = .90 (McHorney, War Jr, et al., 1994; McHorney, Ware, et

al., 1994).

Data

Permission for use of these data was obtained prior to analyses (Appendix B). The

original data set provided for use in this dissertation study did not include personally identifiable

information. A description of how the original data set was created appears below.

Participants

Participants contained in the full data set.

Participants represented in the original data set were recruited from across the United

States. They were adults, over age 18, with mobility impairments who lived in the community

for at least one year. Non-English speaking people, people with less than one year of disability

experience, people with cognitive impairments or psychiatric conditions that prohibited

participation, and people living in institutions were excluded from the original study. Participants

were recruited via mailings from charitable organizations, service provider agencies, and

advocacy organizations as well as through newspaper advertisements and newsletter notices.

Seven hundred and one research packets, including the surveys, project information, and

informed consent requests were mailed to potential participants who responded to the call for

participation and met the inclusion criteria. Six hundred and four responses were received, a

response rate of 86%.

Data from the surveys were coded, identifying information was removed, and data were

entered into SPSS, a statistical software package. The data set was checked to insure accuracy.

Data coding, input, and checking were performed by Dr. Gray’s research team. Dr. Gray

provided the SPSS data file, minus any personally identifiable participant information, for use in

this dissertation study.

Participants in this dissertation study.

The original dataset included people with mobility impairments who used various

mobility aids including canes, crutches, walkers, and wheelchairs as well as those who reported

difficulty walking up to three blocks without an assistive device. The dissertation study reported

here only includes those individuals from the original dataset who used a wheelchair and the

study only investigates those issues that specifically impact the participation of wheelchair users.

The filtered data set, consisting of only those individuals who used a wheelchair, contained 302

participants (n=302.)

Data Analysis

Data were analyzed using SPSS-PC for Windows Release 20.0. Upon receipt, the data set

was examined for missing data using procedures described by the instrument authors to address

missing data, as appropriate.

Descriptive analysis was performed on the participants’ demographic information. This

descriptive analysis focused on the characteristics that were identified as covariates of

participation through in-depth discussion between this author and her advisors (Table 2).

Table 2. Potential Covariates

 Wheelchair type

 Age

 Gender

 Race/ethnicity

 Marital status

 Education

 Personal and household income

 Housemates

 Years in present living arrangement

 Primary condition causing mobility impairment

 Time since onset of condition

 Other conditions in past 30 days

 Amount and type of help needed

 Transportation used

 Primary location

Three variables were derived from the PARTS/M: Extent, Satisfaction, and Importance.

Four variables were derived from the SF-36: Vitality, Social Function, General Mental Health ,

and Emotional Role Function. One variable, Perceived Control Over One’s Life, was derived

from the PIP and one variable, Perception of Reintegration to Social Activities, was derived from

the RNLI. The label for each of these variables came directly from the subscale name. Each

variable was calculated using the appropriate instrument’s scoring system.

Variables derived from the PARTS/M.

The PARTS/M subscales were the source of the variables extent of participation,

importance and satisfaction with participation. Standardized scores were calculated for extent,

importance and satisfaction.

Extent of Participation.

Extent of participation was determined using the PARTS/M items regarding the frequency

of participation in each of the six domains of interest: active recreation, leisure activities,

vacationing, socializing, religious activities, and community activities. Some scales included

multiple items (such as active recreation and leisure activities) and some included only one item

(such as socializing and religious activities). For the active recreation scale, frequency scores for

the individual activities (swimming, playing golf, skiing, racing, bowling, camping, hiking

nature trails) were summed and used as the variable determining the standardized score. For the

leisure recreation scale, frequency scores for the individual activities (dining out, attending

movies, attending concerts, playing cards, playing board games, watching sports, reading) were

summed. Each of the frequency scales contained a single item. Scores were standardized to

address the differences in the number of items in each domain. Factor analysis determined that

extent of participation comprised a single factor. The frequency score in each domain was used

as the variable to determine the standardized score. The following equation was used to

determine the standardized scores.

Importance.

Factor analysis was used to compute the coefficients of a principal component score. For

each variable, missing values were replaced with the variable’s mean value. Each domain’s

importance score was calculated using the following equation.

Factor analysis determined that importance was comprised of two categories which are

named cerebral importance and dynamic importance.

Satisfaction with Participation.

Factor analysis was used to compute the coefficients of a principal component score. For

each variable, missing values were replaced with the variable’s mean value. Each domain’s

satisfaction score was calculated using the following equation.

Factor analysis determined that satisfaction was comprised of a single category.

Variable derived from the PIP.

Perceived control over one’s life was determined using the Part 1, Control subscale of the

PIP. Scoring consisted of a simple summation of the 10 items in the subscale with a possible

total score of 50.

Variable derived from the RNLI.

Perception of integration to social activities was calculated as the adjusted score on the

RNLI. The adjusted score of this assessment was calculated using the following equation:

Variables derived from the SF-36.

The scores for vitality, social function, general mental health, and emotional role function

were calculated according to the SF-36 scoring guidelines. It was first determined whether

enough data were present to calculate the score. If at least half of the items in a subscale were

present in the data set, that participant’s scores could be used and the missing values were

replaced with the mean score for that variable. Some of the item scores were then transformed

such that they all reflected an undesirable characteristic as a low score and a desirable

characteristic as a high score. For instance, the subscale of vitality contained four items and the

score of 2 items were reverse scored (Figure 1).

Figure 1. Sample of SF-36 subscale and item score

How much time during the past

4 weeks:

All of

the

time

Most of

the time

A good bit

of the time

Some of

the time

A little

of the

time

None of

the time

a. Did you feel full of pep?

e. Did you have a lot of energy?

g. Did you feel worn out?

i. Did you feel tired?

Figure 1. For each item, the response “all of the time” is scored 1, “most of the time” is scored 2,

“a good bit of the time” is scored 3, “some of the time” is scored 4, “a little of the time” is scored

5, and “none of the time” is scored 6. In this example, the scores for a. pep and e. energy were

inverted to be on the same scale as the other items. Similar instructions were provided for each

subscale of the SF-36.

Raw scores were calculated as the sum of the scores of the items in the subscale. Each raw

score was then transformed to a 0-100 scale using the following formula:

Once the variables were determined, I discussed the strategies that would be used to

analyze the data with my advisor. Extent and satisfaction were analyzed separately using similar

strategies that are listed below.

Extent of Participation

Research questions 1 and 2 involve determining the significant predictors of the extent of

participation in community-based discretionary activities by wheelchair users. In each of the

following steps extent of participation was the dependent variable. Ordinary Least Squares

regression was used in all analyses of the research questions.

1. Backward elimination regression analysis was used to determine the significant medical

and demographic covariates of extent of participation.

2. Dynamic importance and cerebral importance were included in a subsequent regression

that also included the significant medical and demographic covariates.

3. Non-significant variables were then removed one at a time, removing the least significant

variable at each step, until only significant variables remained.

4. In order to determine the role of each psychosocial factor in predicting the extent of

participation, each one was included in a regression analysis with the significant variables

identified in step 3.

5. In order to investigate covariation among the psychosocial factors, a regression analysis

was performed on all of the significant medical and demographic covariates, cerebral and

dynamic importance, and all of the psychosocial factors.

6. The non-significant variables were then removed from the model one at a time, removing

the least significant variable at each step, until only the significant predictors of the extent

of participation remained.

Satisfaction with Participation

Research question 3 involves determining the significant predictors of satisfaction with

participation in community-based discretionary activities by wheelchair users. In each of the

following steps satisfaction with participation was the dependent variable.

1. Backward elimination regression analysis was used to determine the significant medical

and demographic covariates of satisfaction with participation.

2. Dynamic importance and cerebral importance were included in a subsequent regression

that also included the significant medical and demographic covariates.

3. Non-significant variables were then removed one at a time, removing the least significant

variable at each step, until only significant variables remained.

4. In order to determine the role of each psychosocial factor in predicting satisfaction with

participation, each one was included in a regression analysis with the significant variables

identified in step 3.

5. In order to investigate covariation among the psychosocial factors, a regression analysis

was performed on all of the significant medical and demographic covariates, cerebral and

dynamic importance, and all of the psychosocial factors.

6. The non-significant variables were then removed from the model one at a time, removing

the least significant variable at each step, until only the significant predictors of

satisfaction with participation remained.

The final step was to integrate these two sets of regression analyses into one model of

participation in discretionary, community-based activities by people who use wheelchairs.

Results

Participants

Data from 302 participants were analyzed. The mean age of participants was 49.42 years

(SD 15.66; range 17- 83 years, skewness 0.033, kurtosis -.65) (Table 3). Of these participants,

44.7% were male and 55.3% were female; 88.1% were white, 9.9% were black or African

American, and the remaining 2% reported being from another race or ethnicity or selected the

response “other”. Ninety one percent of the sample had at least a high school education or GED

(Figures 2, 3, and 4). Regarding marital status, 42.7% of the participants were married or part of

an unmarried couple and 18.8% were separated, divorced, or widowed (Figure 5) and 17.9%

reported having children living at home (Table 4). Participants’ reported a wide range of incomes

with 44.4% reporting their annual income to be less than $25,000 (Figure 6).

The participants reported living in a variety of housing situations; 52.6% lived in a house

they owned, 13.9% reported living in a house owned by someone else, 2.3% reported renting a

house, 2.6% reported living in a multiple family dwelling, 18.5% in an apartment, 0.7% in a

dormitory, 0.7% in a transitional living facility, 1% in a hospital or nursing home, and 7.3%

responded “other” to the question (Table 4). One person selected the option “refused” on the

survey. The mean amount of time the participants reported living at their current location was

16.07 years (SD, 14.22; range <1 - >61 years, skewness 1.088, kurtosis .393) (Table 3).

Participants reported using both private and public transportation within their communities; 47%

owned a car or van or had independent means of transportation, 36.4% used public transportation

or friends for transportation, 13.6% depended on rides from family or friends when they were

available, 1% reported seldom traveling and having no reliable source of transportation, and 2%

reported that they did not know or they did not respond (Table 4).

Table 3. Continuous Variables: Age, years at present living situation, years since onset of the

disability.

Variable N Mean (SD) Range Skewness Kurtosis

Age in years 302 49.42 (15.66) 17 - 83 0.033 -0.650

Years of present living

situation

289 16.07 (14.28)

< 1 to > 61 1.0888 0.393

Y e a rs

s in

c e

o n se

t o f

d is

a b il

it y

Spinal Cord

Injury 89 11.27 (9.92) 0.71 - 41.16 1.335 1.068

Multiple Sclerosis 56 19 (12.69) 3.47 - 69.93 1.55 3.52

Cerebral Palsy 63 40.08 (13.06) 16.9 - 71.68 .259 -0.083

Polio 65 47.06 (18.68) 2.72 - 79.94 -0.922 0.453

Stroke 16 6.65 (3.85) 2.3 - 14. 48 0.956 0.067

Figure 2. Gender

Figure 3. Race

Figure 4. Education

Figure 5. Marital Status

Figure 6. Income

Table 4. Characteristics of Participants (N=302)

Characteristic Number (%)

Children Living at Home?

Yes 54 (17.9)

No 248 (82.1)

Wheelchair Type Used

Manual wheelchair only 148 (49)

Power wheelchair only 52 (17.2)

Scooter only 26 (8.6)

Manual and power wheelchair 63 (20.9)

Manual wheelchair and scooter 12 (4)

Power wheelchair and scooter 1 (0.3)

Primary Mobility Device Used in the Community

Manual wheelchair 77 (25.5)

Power wheelchair 72 (23.8)

Scooter 22 (7.3)

Ambulation aid (cane, crutches, walker) 39 (12.9)

Multiple devices 92 (30.5)

Assistance Providers (includes reports of multiple providers)

Relatives 122 (40.4)

Friends 46 (15.2)

Persons hired by participant 68 (22.5)

Persons sent by an agency 66 (21.9)

Other 31 (10.3)

No assistance providers reported 65 (21.5)

Type of Residence

Owns Home 159 (52.6)

Lives in a home somebody else owns 42 (13.9)

House, rented 7 (2.3)

Multiple Family Dwelling 8 (2.6)

Apartment 56 (18.5)

Dorm 2 (0.7)

Transitional Facility 2 (0.7)

Hospital or Nursing Home 3 (1)

Other 22 (7.3)

No response 1 (0.3)

Transportation

Owns car or van or has independent means of

transportation

142 (47)

Uses public transportation or friends for

transportation

110 (36.4)

Depends on rides from friends/family when available 41 (13.6)

Seldom/never travel, no reliable source of

transportation

3 (1)

No response 5 (1.7)

Don’t know 1 (0.3)

All participants were wheelchair users; 49% reported using a manual wheelchair only,

17.2% reported using a power wheelchair only, and 8.6% reported using a scooter only (Table

4). While most wheelchair users use only one type of wheelchair, some use multiple devices for

a variety of reasons including differences in their ability to maneuver in different environments.

For example, sometimes people use power wheelchairs or scooters in the community where

spaces are larger and travel distances are longer especially if their endurance for pushing their

manual wheelchair is limited. They may use a manual wheelchair in their home because they

either are able to manage it sufficiently in that environment or the environment is too confined to

use a power wheelchair or scooter. Some people use multiple devices because their conditions

vary from day to day and even throughout the day. These individuals may use manual

wheelchairs when their coordination and strength are sufficient for propulsion and use powered

wheelchairs or scooters at other times. Of the 302 participants in this sample, 20.9% reported

using both a manual and power wheelchair, 4% reported using both a manual wheelchair and a

scooter, and 0.3% reported using both a power wheelchair and a scooter (Table 4). However,

participants’ reasons for using multiple devices were not included in the data set. Participants

were also asked to identify their primary means of mobility in the community with 25%

reporting that they primarily used a manual wheelchair in the community. Of the remaining

participants, 23.8% reported they primarily used a power wheelchair, 7.3% reported using a

scooter, 12.9% reported using primarily ambulation aids like canes, crutches, and walkers, and

30.5% reported using more than one device in the community (Table 4).

The participants presented with various diagnoses requiring the use of a wheelchair for

community mobility; 29.8% with spinal cord injury (SCI), 19.9% with multiple sclerosis (MS),

22.5% with cerebral palsy (CP), 21.9% with polio, and 6% with stroke (CVA) (Figure 7).

Participants reported the onset of their diagnosis as ranging from < 1 year to > 79 years (Table

3). These data demonstrate normality with the exception of years since onset of the diagnosis of

MS which is kurtotic (kurtosis is 3.52) due to the presence of one outlier. When this participant

is removed from the data set, the distribution is normal (skewness = 0.823, kurtosis = -0.075).

Figure 7. Primary Diagnosis Leading to Wheelchair Use

Note. SCI= Spinal Cord Injury. MS= Multiple Sclerosis. CP= Cerebral Palsy

The data set does not differentiate between the time of onset of the disability and the time

when the participant began using a wheelchair. The time of onset of the disability is the reference

point for this study and ranges from < 1 year to > 79 years. Information regarding the time since

onset by disabling condition can be found in Table 3. These data are normally distributed.

In addition to their medical diagnoses, people with disabilities often have other

conditions that impact their ability to function. In this study, pain, spasticity, skin problems, and

depression were considered as factors that might influence participation in community-based,

discretionary activities. The incidence and frequency of these conditions are reported in Figures

8 through 12.

The conditions that may limit community participation are not diagnosis-specific. For

instance, pain may result directly from nerve damage associated with a certain diagnosis but, for

wheelchair users, it may also result from other factors such as remaining in a given seated

position for an extended period of time. People with any of the diagnoses identified in this

participant pool (SCI, MS, CP, polio, or stroke) may be unable to reposition themselves in their

wheelchairs without help so pain from poor positioning can be associated with any of these

diagnoses. As noted in Figure 8, 71.2% of the participants reported having pain. Of those who

reported the presence of pain, 41.9% reported that their pain was constant (Figure 9).

Spasticity is the presence of involuntary muscle contractions. Spasticity can be painful or

it can restrict movement leading to difficulty in movement and/or avoidance of participation.

Spasticity may also make positioning difficult. If a person is unable to sit appropriately in his or

her wheelchair or is unable to propel his or her wheelchair when spasticity is present, he or she

may avoid participation. Of the participants in this study, 51.3% reported experiencing spasticity

(Figure 8). Of those who reported the presence of spasticity, 31% reported that their spasticity

was constant (Figure 10).

Figure 8. Incidence of Other Conditions

Figure 9. Frequency of Pain (n=215)

Figure 10. Frequency of Spasticity (n=155)

Skin problems, such as ulcers, can develop when a person remains in a single position for

a long period of time. Impaired sensory perception may also prevent the wheelchair user from

knowing he or she is developing skin problems. Low muscle tone may cause excessive pressure

on the seating surface and lead to skin ulcers. Other causes of skin problems include sitting in

sub-optimal positions and sitting without appropriate cushions. People with active skin ulcers in

areas associated with sitting are instructed to stay out of the seated position to allow the ulcers to

heal so they may defer participation in activities that require sitting. Additionally, people with a

history of skin ulcers may be more prone to development of ulcers in the future due to changes in

the condition of their skin and other soft tissue at the ulcer site. These individuals may therefore

avoid discretionary participation reserving their limited sitting time for critical or mandatory

activities. As is reported in Figure 8, 34.8% of the participants in this study reported skin

problems. Of those who reported skin problems, 20% reported that this problem was constant

(Figure 11).

Figure 11. Frequency of Skin Problems (n=105)

People with depression often participate in discretionary activities with less frequency

and less vigor than those without symptoms of depression. It follows then that wheelchair users

with depression may also limit their participation in community-based discretionary activities. Of

the participants in this study, 43.4% reported that they had depression (Figure 8). Of those who

reported the presence of depression, 18.3% reported that the depression was constant (Figure 12).

Only 13 individuals reported no pain, spasticity, skin problems, or depression. Eighty-

four people (27.81%) reported one of these four conditions. It is important to note that these

conditions are not mutually exclusive. An additional 205 participants reported experiencing two

or more of these four conditions.

Figure 12. Frequency of Depression (n=131)

Most of the participants reported that they required assistance during their daily routines

with 23.2% using more than 30 hours of assistance per week, 4.6% using 21-30 hours of

assistance, 11.3% using 11-20 hours, 14.6% using 5-10 hours, 21.5% using less than 5 hours, and

21.5% using no assistance (Figure 13). Additionally, 2.6% of the participants did not report the

amount of assistance used, 0.3% responded “don’t know”, and 0.3% responded “NA”. Those

who did report using assistance identified their sources of help as follows: 40.4% used assistance

from relatives, 15.2% from friends, 22.5% from persons they hired, 21.9% from persons

provided by an agency, and 10.3% from other sources (Table 2). Additionally, 2% selected

“refused” and 0.7% responded “NA”.

Figure 13. Amount of Assistance Received Per Week

Access to appropriate transportation may also be associated with participation in

community-based activities. Many wheelchair users cannot easily move from their wheelchair to

a car seat so they use an accessible vehicle that allows them to remain in their wheelchairs when

being transported. For many, accessible vans are prohibitively expensive and, without a van or

access to accessible public transportation, community participation may be limited. As is

reported in Table 2, 47% of the study participants either own a vehicle or have independent

means of transportation while 36.4% use public transportation and 13.6% rely on rides from

family and friends when they are available.

Outcome and Explanatory Variables

The study variables were derived from subscales of four instruments: The Reintegration

to Normal Living Index (RNLI), The Personal Independence Profile (PIP), The Medical

Outcomes Study Short Form (SF-36), and The Participation Survey/Mobility (PARTS/M). The

variables included perception of reintegration to social activities, perceived control over one’s

life, vitality, general mental health, social function, emotional role function, extent of

participation, dynamic importance, cerebral importance, and satisfaction with participation.

Descriptive statistics for these variables can be found in Table 5. The variable identified as

perception of reintegration to social activities was calculated as the adjusted score on the RNLI.

With 292 cases, the mean score on the RNLI was 76.01 (SD 19.19; range 10 - 100, skewness

-1.134, kurtosis 1.41). Perceived control over one’s life was determined using the control

subscale of the PIP. With 258 cases, the mean score of this subscale was 36.83 (SD 8.09; range

13 - 50, skewness -0.421, kurtosis -0.275.

Table 5. Descriptive Statistics Regarding the Outcome and Explanatory Variables

Variable (source) N Mean (SD) Range Skewness Kurtosis

Perceived Reintegration to

Social Activities (RNLI)

292 76.01 (19.19) 10 - 100 -1.134 1.41

Perceived Control Over

One’s Life (PIP)

258 36.83 (8.09) 13 - 50 -0.421 -0.275

Vitality (SF-36) 295 51.66 (20.15) 0 - 93.33 -0.993 0.266

General Mental Health

(SF-36)

298 72.47 (18.63) 4 - 100 -0.929 0.667

Social Function (SF-36) 292 65.92 (21.84) 10 - 90 -0.559 -0.635

Role Emotional Function

(SF-36)

260 78.21 (36.4) 0 - 100 -1.331 0.13

Extent (PARTS/M) 207 -.1148 (3.31065) -7.36 –

7.94

.258 -.684

Dynamic Importance

(PARTS/M)

302

6.98 (2.4709)

4 - 16

.911 .570

Cerebral Importance

(PARTS/M)

302 3.9625 (1.44889) 2 - 8 .831 .588

Satisfaction (PARTS/M 302 0.1166 (3.33671) -6.79 - 7.34 -0.096 -0.463

Four variables were calculated as scores of subscales on the SF-36: vitality, social

function, general mental health, and emotional role function. Each of these subscales contains

the transformed score with a possible range of 0-100. A higher score on each of the scales

indicates a higher perception of this quality. In order to be scored, a participant must have

completed more than half of the items in that subscale. With 295 cases meeting criteria for

inclusion, the mean vitality score was 51.66 (SD 20.15; range 0 - 93.33, skewness -0.993,

kurtosis 0.266). With 298 cases meeting inclusion criteria, the mean general mental health score

was 72.47 (SD 18.63; range 4 -100, skewness -0.929, kurtosis 0.667). With 292 cases meeting

inclusion criteria, the mean social function score was 65.92 (SD 21.84; range 10 - 90, skewness -

0.559, kurtosis -0.635). With 260 cases meeting inclusion criteria, the mean emotional role

function score was 78.21 (SD 36.4; range 0 - 100, skewness -1.33, kurtosis 0.13). All of these

variables are normally distributed.

Three variables were derived from the PARTS/M: extent of participation in select

community-based activities, satisfaction with performance in select community-based activities,

and importance of participation in select community-based activities which was analyzed as two

factors: dynamic importance and cerebral importance. The selected community-based activities

used for determining these scores were Active Recreation, Leisure Activities, Vacationing,

Socializing, Religious Activity, and Community Activity. These domains were measured using

four scales (Table 6). On each scale, a higher score indicated a greater degree of participation. A

principal components factor analysis indicated that there was a single factor comprising the

extent of participation (Table 7). Because extent comprised subscales with an unequal number of

items, domain-based frequency scores were standardized. Table 8 contains the descriptive

statistics of the unstandardized and standardized extent scores for each domain of interest. Extent

scores were calculated by summing the standardized scores of each domain of interest.

Table 6. Scoring Scales of Extent of Participation by Domain

Score

Domain (s) 1 2 3 4

Active Recreation

and Leisure

Activities

Never 1 -2 times/month 1 -2 times/week More than

twice/week

Taking Vacations Never

Less than

once/year

Once or

twice/year

More than

twice/year

Socializing Less than

once/week 1 - 2 times/week 3 – 4 times/week

Daily or almost

daily

Religious and

Community

Activities

None 1 – 5 hours/week More than 5

hours/week

(not used in

scale)

Table 7. Extent Factor Analysis Structure Matrix

Component

standardized leisure

activities frequency .610

standardized vacation

frequency .521

standardized active

recreation .615

standardized socializing

frequency .573

standardized

community activity

frequency

.616

standardized religious

activity frequency .433

Extraction Method: Principal

Component Analysis. One

component extracted

Table 8. Descriptive Statistics Regarding Extent of Participation in Selected Community-based

Activities of the PARTS/M

Unstandardized Scores Standardized Scores Variable N Mean

(SD)

Range Skew

ness

Kurtosis N Mean

(SD)

Range Skew

-ness

Kurtosis

Active

recreation

270 8.97

(1.48)

8-15 1.727 2.969 270 -.0023

(1.00295)

-.66 –

4.07

1.727 2.969

Leisure

activities

260 14.00

(2.9)

7-22 .097 -.202 260 .0013

(.99973)

-2.41-

2.76

.097 -.202

Vacationing 293 2.36

(.982)

1-4 .018 -1.065 293 -.0017

(.99963)

-1.38-

1.67

.018 -1.065

Socializing 295 2.84

(1.123)

1-4 -.313 -1.375 295 .0036

(1.00007)

-1.64-

1.03

-.313 -1.375

Religious

activities

269 1.64

(.585)

1-3 .271 -.684 269 .0053

(1.00013)

-1.09 –

2.32

.271 -.684

Community

activities

268 1.51 (.590 1-3 .673 -.505 268 .0020

(1.00013)

-.86-

2.53

.673 -.505

The importance of participation was measured in each domain of interest using the

following scale: 1 = very important, 2 = somewhat important, 3 = somewhat unimportant, and 4

= not important. Unlike the extent score, a lower score on this scale reflected greater importance.

Descriptive data regarding the importance of participation in the domains of interest can be

found in Table 9. Principal components factor analysis indicated that there were two factors in

importance (Table 10). The two domain groupings are based on qualities related to movement

required versus their community-mindedness. For this reason, two importance variables dynamic

importance and cerebral importance were named. Dynamic importance was calculated as the

sum of the importance scores of the following domains: active recreation, leisure activities,

vacations, and socialization. Cerebral importance was calculated as the sum of the importance

scores in the domains of religious activities and community activities. Descriptive statistics

regarding dynamic importance and cerebral importance scores can be found in Table 5.

Table 9. Descriptive Statistics Regarding Importance of Participation

Variable N Mean (SD) Range Skewness Kurtosis

Active recreation 302 2.22 (1.036) 1 - 4 .504 -.857

Leisure activities 302 1.58 (.787) 1 - 4 1.486 1.955

Vacationing 302 1.67 (.853) 1 - 4 1.335 1.172

Socializing 302 1.51 (.657) 1 - 4 1.186 1.136

Religious activities 302 1.78 (.948) 1 - 4 1.263 .632

Community activities 302 2.9 (.910) 1 - 4 .508 -.366

Table 10. Importance Factor Analysis Structure Matrix

Component

1 2

Importance - Leisure activities .788

Importance - Take a vacation .708

Importance – Socializing .690 .337

Importance - Active recreational

activities .682

Importance - Religious activities .825

Importance - Community activities .323 .714

Note. Extraction Method: Principal Component Analysis.

Rotation Method: Promax with Kaiser Normalization.

Importance Factor Analysis Component Correlation Matrix

Component 1 2

1 1.000 .253

2 .253 1.000

Note. Extraction Method: Principal Component Analysis. Rotation Method: Promax

with Kaiser Normalization.

Satisfaction with participation was scored using the following scale: 1= very satisfied, 2

= satisfied, 3 = somewhat satisfied, 4 = dissatisfied. Therefore, a lower score reflected greater

satisfaction. Descriptive statistics regarding satisfaction with participation in the domains of

interest can be found in Table 9. Factor analysis determined that there was one factor in

satisfaction (Table 10).

Table 11. Descriptive Statistics Regarding Satisfaction with Participation

Variable N Mean (SD) Range Skewness Kurtosis

Active recreation 302 2.99 1 - 4 -.664 -.625

Leisure activities 302 2.40 1 - 4 .036 -.904

Vacationing 302 2.61 1 - 4 .140 -1.033

Socializing 302 2.30 1 - 4 .225 -1.042

Religious activities 302 2.19 1 - 4 .413 -.551

Community activities 302 2.41 1 - 4 .146 -.527

Table 12. Satisfaction Factor Analysis Component Matrix

Component

Satisfaction - Socializing .822

Satisfaction - Leisure activities .809

Satisfaction - Active recreational activities .740

Satisfaction – Vacationing .726

Satisfaction - Community activities .701

Satisfaction - Religious activities .545

Note. Extraction Method: Principal Component Analysis. One component

extracted. The solution cannot be rotated.

The mean extent score was -.1148 (n = 207, SD 3.31065, range -7.36 – 7.94, skewness

.258, kurtosis -.684). The mean dynamic importance score was 6.98 (n = 302, SD 2.4709, range

4 - 16, skewness .911, kurtosis .570). The mean cerebral importance score was 3.9625 (n = 302,

SD 1.44889, range 2 - 8, skewness .831, kurtosis .588). The mean satisfaction score was 0.1166

(n = 302, SD 3.33671, range -6.79 - 7.34, skewness -0.096, kurtosis -0.463) (Table 5). These

variables were normally distributed.

Findings Regarding the Extent of Participation in Community-based, Discretionary

Activities by People who Use Wheelchairs

Research questions 1 and 2 concern the extent of participation. The first step in

determining the role of psychosocial function in predicting participation in community-based,

discretionary activities was to determine the significant medical and demographic covariates.

Backward elimination regression analysis was used to determine the significant medical and

demographic covariates of the extent of participation. Years in the present living situation and a

diagnosis of cerebral palsy as the condition leading to the need for a wheelchair were positively

related to the extent of participation. In addition, education to grades 1 through 8 or grade 12 or

GED were identified as predictors of non-participation at a significant level. A diagnosis of

stroke as the condition leading to the need for a wheelchair predicted non-participation at a

significant level (Table 13).

Table 13. Regression Analysis of Medical and Demographic Covariates of Extent of

Participation

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Years in Present

Living

Arrangement

.058 .021 .250 2.772 .006

Grade 1 – 8 -4.390 2.162 -.160 -2.031 .044

Grade 12 or GED -1.899 .630 -.243 -3.014 .003

Primary

Condition- CP 3.584 .848 .376 4.224 .000

Primary

Condition- Stroke -2.268 1.090 -.179 -2.081 .039

Note. R 2 = .259.

In addition to these medical and demographic covariates, it is likely that importance plays

a role in the extent to which a person participates in the activities of interest in this dissertation.

Therefore, a regression analysis was performed with the significant medical and demographic

covariates and the two importance variables. When dynamic importance and cerebral

importance were added to the regression model, they were found to be predictors of extent of

participation (Table 14). The only previously identified medical or demographic covariate that

remained significant was a diagnosis of cerebral palsy leading to wheelchair use.

Table 14. Regression Analysis of Covariates of Extent of Participation

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Years in Present

Living

Arrangement

.028 .016 .117 1.778 .077

Grade 1 – 8 -2.197 1.335 -.104 -1.646 .101

Grade 12 or GED -.932 .507 -.120 -1.838 .068

Primary

Condition- CP

1.913 .547 .233 3.500 .001

Primary

Condition- Stroke -1.666 .950 -.115 -1.753 .081

Dynamic

Importance* -.381 .094 -270 -4.036 .000

Cerebral

Importance* -.549 .141 -.251 -3.888 .000

Note. R 2 = .286. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

The next step in determining the psychosocial predictors of participation was to remove

the variables that were not significant and to analyze the results. Each non-significant variable

was removed from the analysis one at a time until only significant variables remained. Non-

significant variables were removed such that the least significant was removed at each level of

the model. Table 15 shows the order by which the variables were removed as well as their

significance when they were removed. In the final model, the following variables remained: a

diagnosis of cerebral palsy, dynamic importance, and cerebral importance (Table 16). This

indicates that they are significant predictors of the extent of participation. A diagram representing

the model at this point can be found in Figure 14.

Table 15. Order of Removal and Significance of Non-significant Covariates of Extent of

Participation

Variable (in order of removal) Significance (at time of

removal from model)

Adjusted R 2

Grade 1 - 8 .101 .286

Grade 12 or GEC .111 .279

Years of living situation .133 .273

Stroke .110 .271

Table 16. Extent: Significant Covariate Predictors

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

1.699 .485 .214 3.504 .001

Dynamic

Importance* -.395 .089 -.281 -4.428 .000

Cerebral

Importance* -.564 .136 -.261 -4.132 .000

Note. R 2 = .265. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Figure 14. A Model Predicting the Extent of Participation by Wheelchair Users in Community-

based, Discretionary Activities.

The next step in determining the role of psychosocial factors in predicting the extent of

participation was to include each of the six psychosocial factors (social function, general mental

health, emotional role function, vitality, perceived control, and reintegration to social function)in

the model one at a time. Each psychosocial factor was entered individually to isolate the effects

of each factor. In each regression, each psychosocial factor also was shown to be a significant

predictor of extent. All of the medical and demographic covariates remained significant

predictors of extent (Tables 17 – 22).

Extent of Participation Cerebral Cognition

Dynamic Cognition

Cerebral Palsy

B=1.699

(0.485)

B=1.699

(0.485)

B=1.699

(0.485)

Table 17. Extent: Regression Analysis of Covariates and Social Function

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

1.002 .499 .126 2.008 .046

Dynamic

Importance* -.362 .087 -.257 -4.182 .000

Cerebral

Importance* -.579 .132 -.267 -4.393 .000

Social Function .036 .009 .249 4.005 .000

Note. R 2 = .286. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Table 18. Extent: Regression Analysis of Covariates and General Mental Health

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

1.489 .478 .187 3.116 .002

Dynamic

Importance* -.403 .087 -.287 -4.617 .000

Cerebral

Importance* -.526 .134 -.243 -3.927 .000

General Mental

Health .035 .010 .199 3.369 .001

Note. R 2 = .301. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Table 19. Extent: Regression Analysis of Covariates and Emotional Role Functioning

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

1.674 .505 .211 3.314 .001

Dynamic

Importance* -.372 .092 -.266 -4.027 .000

Cerebral

Importance* -.672 .147 -.300 -4.566 .000

Emotional Role

Functioning .014 .006 .158 2.494 .014

Note. R 2 = .326. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Table 20. Extent: Regression Analysis of Covariates and Vitality

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

1.413 .499 .178 2.828 .005

Dynamic

Importance* -.356 .091 -.253 -3.923 .000

Cerebral

Importance* -.564 .136 -.261 -4.164 .000

Vitality .021 .010 .135 2.127 .035

Note. R 2 = .277. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Table 21. Extent: Regression Analysis of Covariates and Perceived Control

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

2.070 .514 .244 4.028 .000

Dynamic

Importance* -.286 .090 -.200 -3.196 .002

Cerebral

Importance* -.413 .141 -.183 -2.928 .004

Perceived Control .134 .024 .341 5.593 .000

Note. R 2 = .361. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Table 22. Extent: Regression Analysis of Covariates and Perception of Reintegration to Social

Function

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

1.636 .464 .204 3.527 .001

Dynamic

Importance* -.327 .085 -.232 -3.849 .000

Cerebral

Importance* -.483 .130 -.222 -3.705 .000

Perception of

Reintegration to

Social Function

5.712 1.032 .321 5.536 .000

Note. R 2 = .377. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

The next step in the analysis was to include all of the psychosocial factors and all of the

significant medical and demographic variables in a single regression model. This step was

necessary in order to account for any covariation among the psychosocial explanatory factors. In

this model, a diagnosis of cerebral palsy leading to wheelchair use, dynamic importance, and

cerebral importance remained significant. The only psychosocial factors that were found to be

significant predictors were perceived control and perception of reintegration to social function

(Table 23). The non-significant variables were then removed from the model one at a time;

removing the least significant variable at each step until only significant predictors remained in

the model. Table 24 identifies the order by which the variables were removed along with their

significance at the time of removal. The final model of the extent of participation by wheelchair

users in community-based discretionary activities includes the following significant predictors: a

diagnosis of cerebral palsy leading to wheelchair use, dynamic importance, cerebral importance,

perceived control, and perception of reintegration (Table 25, Figure 15).

Table 23. Extent: Regression Analysis of Covariates and All of the Psychosocial Factors

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

2.145 .568 .255 3.777 .000

Dynamic

Importance* -.287 .097 -.203 -2.958 .004

Cerebral

Importance* -.504 .155 -.220 -3.254 .001

General Mental

Health -.002 .015 -.009 -.109 .913

Social Function .016 .014 .112 1.193 .235

Emotional Role

Function .006 .007 .061 .780 .436

Vitality -.023 .014 -.149 -1.688 .094

Perceived Control .085 .037 .207 2.312 .022

Perception of

Reintegration to

Social Function

3.322 1.663 .178 1.997 .048

Note. R 2 = .390. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Table 24. Order of Removal and Significance of Non-significant Covariates of Extent of

Participation

Variable (in order of removal) Significance (at time of

removal from model)

Adjusted R 2

General Mental Health .913 .390

Emotional Role Functioning .425 .394

Vitality .180 .398

Social Function .105 .394

Table 25. Extent: Significant Predictors

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B

Standard

Error Beta

Primary

Condition- CP

2.013 .506 .238 3.975 .000

Dynamic

Importance* -.276 .089 -.193 -3.117 .002

Cerebral

Importance* -.425 .136 -.189 -3.054 .003

Perceived Control .086 .032 .218 2.720 .007

Perception of

Reintegration to

Social Function

3.634 1.436 .19 2.531 .012

Note. R 2 = .388. *The scales for Dynamic and Cerebral Importance are written such that a high

score indicates low importance therefore a negative value indicates positive covariation with

extent.

Figure 15. A Model Predicting the Extent of Participation by Wheelchair Users in Community-

based, Discretionary Activities.

B= 3.634

(1.436)

B= -.425

(0.136)

B= .086

(0.032)

B= -.276

(0.089)

B= 2.013

(0.506)

Extent of Participation

Perception of Reintegration to

Social Function

Perceived Control

Cerebral Importance

Dynamic Importance

Findings Regarding Satisfaction with Participation in Community-based, Discretionary

Activities by People who Use Wheelchairs

Research question 3 concerns satisfaction with participation. The first step in determining

the role of the psychosocial factors in predicting satisfaction was to determine the significant

medical and demographic covariates. Backward elimination regression analysis was used to

determine the significant medical and demographic covariates of satisfaction with participation.

Participants diagnosed with cerebral palsy as the condition leading to wheelchair use and those

who reported greater participation were satisfied with their participation at a significant level.

Those with spasticity and those with depression were not satisfied with their participation at a

significant level (Table 26).

Table 26. Regression Analysis of Medical and Demographic Covariates of Satisfaction with

Participation

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

Cerebral Palsy -1.847 .786 -.188 -2.350 .020

Spasticity -1.004 .502 .141 1.999 .048

Depression -1.641 .500 .238 3.279 .001

Extent of

Participation -.372 .077 -.361 -4.861 .000

Note. R 2 = .259. The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

In addition to these medical and demographic covariates, it was necessary to determine

whether importance played a role in the satisfaction with participation in the activities of interest

in this dissertation. Therefore, a regression analysis was performed with the significant medical

and demographic covariates and the two importance variables. When dynamic importance and

cerebral importance were added to the model, they were found not to be predictors of

satisfaction (Table 27). The only previously identified medical or demographic covariate that

remained significant was a diagnosis of cerebral palsy leading to wheelchair use.

Table 27. Regression Analysis of Covariates of Satisfaction with Participation

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

CP -1.153 .560 -.141 -2.058 .041

Spasticity .831 .405 .121 2.051 .042

Depression 1.167 .420 .171 2.781 .006

Extent of

Participation

-.463 .072 -.449 -6.402 .000

Uses public

transportation or

relies on friends

-.831 .462 -.117 -1.800 .073

Dynamic

Importance -.147 .094 -.101 -1.557 .121

Cerebral

Importance -.116 .143 -.052 -.812 .418

Note. R 2 = .302. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

The next step in determining the roles of the variables was to remove the variables that

were not significant and to analyze the resulting significance. Each non-significant variable was

removed from the analysis one at a time until only significant predictors remained. Non-

significant variables were removed such that the least significant was removed at each level of

the model. Table 28 shows the order in which the variables were removed as well as their

significance when they were removed. In the final model, the following variables remained:

cerebral palsy, depression, and extent of participation (Table 29). This indicates that they are

significant predictors of satisfaction with participation. A diagram representing the model at this

point can be found in Figure 16.

Table 28. Order of Removal and Significance of Non-significant Covariates of Satisfaction with

Participation

Variable (in order of removal) Significance (at time of

removal from model)

Adjusted R 2

Cerebral Importance .418 .302

Dynamic Importance .085 .303

Public Transportation or

Friends

.098 .296

Spasticity .053 .291

Table 29. Satisfaction: Significant Covariates

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

CP -1.481 .520 -.181 -2.850 .005

Depression 1.313 .421 .193 3.117 .002

Extent of

Participation -.378 .064 -.367 -5.870 .000

Note. R 2 = .265. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Figure 16. A Model Predicting Satisfaction with Participation in Community-based,

Discretionary Activities by Wheelchair Users.

Satisfaction with

Participation Depression

Extent

Cerebral Palsy B= 1.481

(0.520)

B= 1.313

(0.421)

B= -0.378

(0.064)

The next step in determining the role of psychosocial factors in predicting satisfaction

with participation was to include each of the six psychosocial factors in the model one at a time.

Each psychosocial factor was entered individually to isolate the effects of each factor. General

mental health, social function, vitality, control, and perception of reintegration to social function

were significant in each of their respective models. In the models for vitality and the perception

of reintegration to social functioning, all of the covariates previously identified as significant

remained significant. In the models for general mental health, social function, and control,

depression was no longer found to be a significant predictor; only a diagnosis of cerebral palsy

leading wheelchair use and extent of participation remained significant. In the model that

included emotional role functioning, that psychosocial factor was not found to be a significant

predictor but all three of the previously identified covariates remained significant (Tables 30 –

35).

Table 30. Satisfaction: Regression Analysis of Covariates and Social Function

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

CP -1.038 .519 -.127 -2.001 .047

Depression .817 .431 .120 1.895 .060

Extent of

Participation -.322 .064 -.312 -4.995 .000

Social Function -.037 .010 -.246 -3.652 .000

Note. R 2 = .322. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Table 31. Satisfaction: Regression Analysis of Covariates and General Mental Health

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

Primary

Condition- CP

-1.594 .494 -.195 -3.228 .001

Depression .268 .453 .039 .593 .554

Extent of

Participation -.320 .062 -.311 -5.145 .000

General Mental

Health -.059 .012 -.325 -4.959 .000

Note. R 2 = .356. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Table 32. Satisfaction: Regression Analysis of Covariates and Emotional Role Functioning

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

CP -1.685 .533 -.213 -3.160 .002

Depression 1.046 .464 .156 2.253 .026

Extent of

Participation -.367 .067 0.367 -5.484 .000

Emotional Role

Functioning -.006 .006 -.064 -.947 .345

Note. R 2 = .302. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Table 33. Satisfaction: Regression Analysis of Covariates and Vitality

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

Primary

Condition- CP

-1.177 .519 -.144 -2.262 .024

Depression 1.010 .425 .148 2.376 .018

Extent of

Participation -.341 .064 -.331 -5.303 .000

Vitality -.032 .010 -.202 -3.183 .002

Note. R 2 = .312. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Table 34. Satisfaction: Regression Analysis of Covariates and Perceived Control

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

Primary

Condition- CP

-1.699 .517 -.198 -3.286 .001

Depression .666 .399 .099 1.668 .097

Extent of

Participation -.153 .066 -.151 -2.305 .022

Perceived Control -.190 .025 -.477 -7.486 .000

Note. R 2 = .439. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Table 35. Satisfaction: Regression Analysis of Covariates and Perception of Reintegration to

Social Function

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

Primary

Condition- CP

-1.718 .484 -.211 -3.550 .000

Depression .852 .396 .126 2.151 .033

Extent of

Participation -.220 .065 -.217 -3.396 .001

Perception of

Reintegration to

Social Function

-6.572 1.122 -.364 -5.857 .000

Note. R 2 = .390. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

The next step in the analysis was to include all of the psychosocial factors and all of the

significant medical and demographic variables in a single regression model. This step was

necessary to account for covariation among the explanatory predictors. In this model, a diagnosis

of cerebral palsy leading to wheelchair use and the extent of participation remained significant.

Depression was no longer a significant predictor in this model. The only psychosocial factor that

was found to be a significant predictor was perceived control (Table 36). The non-significant

variables were then removed from the model one at a time; removing the least significant

variable at each step until only significant predictors remained in the model. Table 37 identifies

the order by which the variables were removed along with their significance at the time of

removal. The final model of satisfaction with participation in community-based discretionary

activities by wheelchair users includes the following significant predictors: a diagnosis of

cerebral palsy leading to wheelchair use, extent of participation, general mental health, and

perceived control (Table 38, Figure 17).

Table 36. Satisfaction: Regression Analysis of Covariates and All of the Psychosocial Factors

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

Primary

Condition- CP

-1.868 .562 -.226 -3.323 .001

Depression .052 .477 .008 .110 .913

Extent of

Participation -.154 .071 -.157 -2.185 .030

General Mental

Health -.029 .015 -.162 -1.881 .062

Social Function .006 .013 .041 .451 .653

Emotional Role

Function .009 .006 .104 1.433 .154

Vitality -.017 .013 -.114 -1.361 .176

Perceived Control -.151 .035 -.372 -4.312 .000

Perception of

Reintegration to

Social Function

-1.276 1.573 -.069 -.811 .419

Note. R 2 = .452. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Table 37. Order of Removal and Significance of Non-significant Covariates

Variable (in order of removal) Significance (at time of

removal from model)

Adjusted R 2

Depression .913 .452

Social Functioning .655 .456

Perception of Integration to

Social Function

.427 .459

Emotional Role Function .227 .460

Vitality .738 .457

Table 38. Satisfaction: Significant Predictors

Variable

Unstandardized

Coefficients

Standardized

Coefficients

t Sig B*

Standard

Error Beta

Primary

Condition- CP

-1.808 .500 -.211 -3.618 .000

Extent of

Participation -.139 .066 -.137 -2.124 .035

Perceived Control -.170 .026 -.426 -6.476 .000

General Mental

Health -.034 .011 -.191 -3.130 .002

Note. R 2 = .388. * The scale for satisfaction is written such that a high score indicates low

satisfaction. Therefore a negative value indicates positive covariation and a positive value

indicates negative covariation.

Figure 17. A Model Predicting Satisfaction with Participation in Community-based,

Discretionary Activities by Wheelchair Users.

When studying the role of psychosocial function in predicting participation in

community-based, discretionary activities, it was necessary to investigate the extent to which

people participated in community-based activities and their satisfaction with that participation. It

was also necessary to analyze the roles of medical and demographic covariates. The only

B= -0.034

(0.011)

Satisfaction with

Participation

Perceived Control

General Mental Health

Extent of Participation

B= -1.808

(0.500)

B= -0.139

(0.066)

B= -.170

(0.026)

covariate in the final model is a diagnosis of cerebral palsy leading to wheelchair use. In addition

to the diagnosis of cerebral palsy, dynamic and cerebral importance, perceived control over one’s

time, and perception of reintegration to social function predicted the extent to which wheelchair

users participated in community-based, discretionary activities.

In addition to the diagnosis of cerebral palsy leading to wheelchair use, perceived control

over one’s time and an individual’s general mental health (nervousness, sadness, calmness, and

downheartedness) also predicted satisfaction. Perceived control over one’s time played a doubly

important role as it predicted both the extent of participation and satisfaction with participation.

Figure 18 illustrates the model of participation.

Figure 18. A Model of Participation in Community-based, Discretionary Activities.

Perceived Control

Perception of

Reintegration to Social

Function

Dynamic Importance

Cerebral Importance

Perceived Control

General Mental

Health

Extent of

Participation

Satisfaction with

Participation

B= -.425

(0.136)

B= .086

(0.032)

B= 3.634

(1.436)

B=2.013

(0.506)

B= -.276

(0.089)

B= -.170

(0.026)

B= -.034

(0.011)

B= -0.139

(0.066)

B= -1.808

(0.500)

Discussion

The purpose of this research was to determine the role of psychosocial functioning in

influencing participation in community-based, discretionary activities by people who use

wheelchairs. Two participation variables were studied: extent of participation and satisfaction

with participation. This research demonstrates that psychosocial functioning does predict

participation in community-based, discretionary activities. In the past, researchers used variables

related to the extent of participation without also studying satisfaction. For example, Harris et al.

(2010) used the amount of time the wheelchair user spent in his or her wheelchair as a proxy for

participation. They did not, however, study the activities that occurred while the participants

were in their wheelchairs in terms of the participant’s engagement. As a consequence, Harris and

colleagues’ study limited the ability of the researchers to fully describe participation. By

investigating both the extent of participation and satisfaction with participation this dissertation

studied participation more thoroughly.

Other researchers who have studied wheelchair users have focused primarily on physical

factors such as how far or fast a person propelled his or her wheelchair (Chow & Levy, 2011;

Dieruf et al., 2008). Still others focused on self-care, work, and other mandatory activities such

as a getting to a doctor’s appointment; on a mix of mandatory and discretionary activities; or on

broader groups of people including individuals who do not use a wheelchair (M. L. Lund et al.,

2007; Sonenblum et al., 2008). These research studies provide only a partial picture of

participation and neglect to show the role of psychosocial functioning in participation.

Two previous studies identified the need to study the role of psychosocial functioning in

predicting participation and were influential in designing the present dissertation. Chaves et al.

(2004) studied the relationship between the mobility device, the person’s impairments, and three

environments. Among other results, they reported that social attitudes and self-concept limited

the frequency with which participants left their homes. The results of Chaves et al.’s study did

not describe psychosocial functioning sufficiently and did not enable the authors to develop a

comprehensive model of participation.

Crawford et al. (2008) used the PARTS/M, RNLI, and SF-36 to study the relationship

between physical activity and participation in both mandatory and discretionary activities inside

and outside the home. Crawford et al.’s findings, while useful, are incomplete as they identified

the physical activity levels that predicted participation without also including the psychosocial

factors related to participation. Unlike Crawford et al., this dissertation investigated psychosocial

characteristics rather than physical activity levels that predicted participation. By identifying the

psychosocial factors that predict participation in discretionary, community-based activities by

adult wheelchair users, the knowledge base is expanded and suggests that future researchers

include these variables in their studies of this population. To date, comprehensive research such

as this does not exist for adult wheelchair users. King et al. (2003) developed a comprehensive

model of participation that includes physical and environmental factors as well as psychosocial

factors. However, King et al.’s model addresses children with disabilities. This dissertation

research reflects King et al.’s work but includes necessary adjustments to reflect the needs of

adults who use wheelchairs. Knowledge is expanded by focusing on discretionary rather than

mandatory activities and by focusing on community-based rather than home-based activities.

Previous research regarding the role of psychosocial functioning in disabled people’s

participation in various activities has found that self-consciousness and the attitudes of non-

disabled people were factors that influenced participation by disabled people (Card et al., 2006;

Fougeyrollas, Noreau, & Boschen, 2002; Rimmer et al., 2004). These studies and other

psychosocial research projects concerning people with disabilities differ from this dissertation’s

work because they do not differentiate wheelchair users from people with other mobility

impairments or disabilities that do not affect mobility (P. D. A. Gum, C. Snyder, & P. W.

Duncan, 2006; R. W. Motl & E. McAuley, 2010).

As previously noted, two participation variables were studied: extent of participation and

satisfaction with participation. Data analysis on the extent of participation and satisfaction with

participation were performed separately following the same series of steps. First, the significant

medical and demographic covariates were identified through discussions with this researcher’s

mentor and with experts in the field of physical rehabilitation. The list of factors was compared

with the items in the data base and it was determined that the data base contained sufficient

demographic and medical information to perform these analyses. Approximately 20

demographic and medical factors were investigated using regression analysis to determine which

of these factors were significant predictors of extent and satisfaction.

Because the importance of each activity studied was thought to be a determinant of

participation, the activity’s importance was then included in the regression analysis along with

the significant demographic and medical covariates. This was necessary to determine the role

that the importance of the activities played in predicting the extent of participation after

controlling for the covariates. It is interesting that certain demographic and medical covariates

were no longer significant predictors of participation when importance was included in the

model. The following variables were eliminated as predictors of the extent of participation when

importance was added to the model: years in the present living arrangement, education to grades

6-8 and 12 (or GED), and having a primary diagnosis of stroke. A diagnosis of cerebral palsy

was the only medical or demographic covariate that remained a positive predictor of the extent of

participation.

These preliminary analyses were helpful in examining the complicated relationships

among the various medical and demographic covariates. In addition, the results of these analyses

indicated that my assumptions were not supported. For example, through previous clinical work

and discussions with other rehabilitation professions, I assigned much greater importance to the

type of wheelchair a person used and the person’s living situation than was the case. Although

not specifically the purpose of this study, these findings are extremely important to share with

rehabilitation professionals who specialize in seating and mobility and may be indicative of

changes that might be made in prescribing therapies and equipment to wheelchair users. It may

be that, rather than the type of mobility device the wheelchair user has, it is actually whether the

device is the optimal or most appropriate device for the individual that impacts participation.

That variable however, was not included in the data base and as such could not be studied in this

dissertation.

It was surprising that the length of time a person was diagnosed with the condition

leading to their wheelchair use did not predict participation. This researcher expected that

people who recently began using their wheelchairs would participate to a lesser extent than those

who had used their wheelchairs for longer lengths of time because they needed time to acclimate

to their condition and new status as wheelchair users. Apparently, this was not the case. In this

study, length of time since the diagnosis resulting in wheelchair user did not predict either extent

of or satisfaction with participation.

The study data set included information about how much help participants required in

hours per week. It also identified the caregivers. Neither the amount of help nor the identity of

the caregiver predicted participation. Future studies should also account for the types of tasks for

which assistance is needed such as personal versus household care. It is plausible to think that

people who need assistance of a more personal nature, such as, for example, for dressing or

feeding, would have more difficulty participating in discretionary activities than those who need

help with less personal tasks like writing checks to pay bills or shopping for groceries.

Conversely, however, it is also plausible that people who use more assistance might conserve

energy for desired activities and therefore be more able to participate in discretionary activities.

Similarly broad arguments can be made regarding the people who provide assistance. It is

equally plausible that a person may be more active if his or her assistant is a relative or a stranger

dependent on the relationship they develop, the ease with which the wheelchair user can ask for

help, and the strength with which (s)he makes his/her needs known. In this dissertation, the

amount of assistance used and the source of the assistance were not found to be significant

predictors of participation. It may be however, if different questions were asked, the findings

might show that assistance actually does predict participation.

Interestingly, as noted above, the only medical or demographic covariate that predicted

either the extent of participation or satisfaction with participation was a diagnosis of cerebral

palsy leading to wheelchair use. Cerebral palsy predicted both a high frequency (extent) of

participation as well as high satisfaction with participation. It is not obvious why this diagnosis

would predict participation while other diagnoses leading to wheelchair use do not. However, in

a study in Sweden on dependence in daily activities and life satisfaction of people with cerebral

palsy, researchers found their participants reported high satisfaction with life in general even

when the participants needed progressively more assistance with daily living talks and mobility

(Andren & Grimby, 2004). Because they did not investigate participation in discretionary

activities, these findings cannot be generalized to this dissertation. Of the diagnoses leading to

wheelchair use included in this study, cerebral palsy is the only disability present at birth. It may

be that growing up with a disability or as a wheelchair user impacts participation but this is not

clear, especially since the length of time since the onset of the disabling condition was not found

to predict either extent or satisfaction.

In this study, the concept of importance was represented by two variables: cerebral

importance and dynamic importance because factor analysis of this variable revealed that

importance was comprised of two components. Cerebral importance is the label given to the

component comprised of importance related to socializing, religious activities, and community

activities. Dynamic importance is the label given to the component comprised of importance

related to leisure activities, taking a vacation, and active recreational activities. This study

demonstrated that both cerebral importance and dynamic importance are significant positive

predictors of the extent of participation. In order to understand the role of importance, it is

necessary to study the concept in greater detail. Future research needs to address the

characteristics that make an activity important to an individual, such as whether it is the activity

itself or the other people who also participate. It would also be necessary to determine the

reasons that people do not participate in activities they identify as important. There may be other

factors that influence a person’s participation in certain discretionary activities. This dissertation

study was not sensitive enough to determine, for example, whether a person who thought a given

activity was important or not is based on other factors like access to the activity’s location or

finances needed to participate.

This dissertation focused on a broad spectrum of psychosocial factors all of which impact

participation in discretionary activities: social functioning, general mental health, emotional role

functioning, vitality, perceived control over one’s time, and perception of reintegration to social

function. Once the significant demographic and medical covariates (including importance) were

identified, a separate series of regression analyses were performed on the extent of participation

and on satisfaction with participation for each of the psychosocial factors considered

individually. These analyses revealed that every psychosocial factor was a significant positive

predictor of both the extent of and satisfaction with participation with one exception. However,

emotional role functioning was not found to predict satisfaction with participation. It did,

however, positively predict extent of participation.

When all of the psychosocial factors were included in a regression model along with the

significant medical and demographic covariates, only perceived control and perception of

reintegration were found to predict the extent of and satisfaction with participation. As the

participants’ perceived control increased and as their perception of reintegration increased, there

was a corresponding increase in the extent of participation and in the participant’s satisfaction

with their participation. Since the majority of the community-based, discretionary activities

investigated in this study are social, it follows that people in this study who report they have

reintegrated to social function also participate more in such activities and are more satisfied with

their participation.

Although the remaining psychosocial factors did not predict participation when they were

included in the model as a group, they were shown to predict participation when they were

examined in the model individually. This indicates they could have an indirect effect on the

extent of participation and on satisfaction with participation. However, in order to determine this,

it would be necessary to study participation at an earlier time to see the effect of these potential

predictors over time.

Study Limitations

There are limitations to this study that need to be considered when assessing the value of

the results. The sample of this research was diverse with the exception of race/ethnicity in that

88.1% of the participants identified themselves as white whereas, according to US Census data

of 2000, 57.4% of the disabled population was white (US Census Bureau, 2009). There are a few

possible explanations for this. The US Census data include people with all disabilities not just

people who use wheelchairs so this may not be a comparable population. It is also possible that

people of color and of diverse ethnicities participate less in the organizations where recruitment

took place which included Independent Living Centers and national disability, diagnosis, and

condition support groups. In a study comparing participation of children with and without

disabilities in community-based activities, Bedell, et al. also report that 81% of their participants

were white, non-hispanic (Bedell et al., 2013). Bedell identified this as a limitation of their study

as well. It seems evident that better methods are needed to recruit participants characterized by

greater diversity in race and ethnicity. As such, the research findings have limited

generalizability. Cultural norms may influence not only the types of discretionary activities in

which people participate, they also may influence their views of disability and wheelchair use.

Cultural norms may also influence the extent to which people participate in various activities.

Although ethnicity did not predict participation in this study, it may be because of the limited

diversity in the study sample. .

The five medical conditions that led participants in this study to use wheelchairs were

spinal cord injury, stroke, cerebral palsy, polio, and multiple sclerosis. I initially assigned greater

importance to the medical condition leading to wheelchair use as a predictor of participation and

thought that people with different diagnoses would participate to different extents but this turned

out not to be the case. Four of the five diagnoses were fairly evenly represented in the participant

pool (19.9%- 29.8%) but only 6% of the participants were diagnosed with stroke as the condition

leading to wheelchair use. It is not clear from the recruitment strategies why so few people who

had strokes were recruited or agreed to participate. While having had a stroke did not predict

participation in this study, so small a sample may not be representative of the population of

people who had strokes in the areas from which participants were recruited. Additional research

including more stroke survivors may reveal other important findings.

Using a Secondary Source for Data

There are many benefits to using a secondary data set, the first of which is that the data

already exist. In research, this translates into time and money saved. Data collection is time

consuming and costly. Using an existing data set shrinks the amount of time necessary to

complete a study. The data set used in this dissertation included the information needed to

answer the research questions and, as such, was an asset. This data set contains a wealth of

information and should be used to answer additional questions in the future.

Using an existing data set also has the potential to lead to problems because the

researcher may not have access to details of data collection and input that are necessary for full

analysis. Such was the case for this data set. The problems and resulting limitations are discussed

here. One example concerns how the participants reported their living situations. Participants

were asked “how many people do you live with?” Whether participants included themselves or

not in the reported number is not clear. One hundred and seventeen people indicated that they

lived with one person. Of those 117 people, 80 also reported that they were married. While being

married does not necessarily mean that every person lives with his or her spouse, it is likely that

most of the married participants do live with their spouse suggesting that when they responded

that they lived with one person, it was the spouse. The problem is that there was not an option for

participants to indicate that they lived with zero people which would be the appropriate answer

for people living alone. Instead the divorced, widowed, and never been married people also

likely responded that they lived with at least one person. This is important because people living

with a spouse or other housemates may be more likely to participate in discretionary activities

outside the home than those living alone. Without access to reliable data, it is not possible to

infer participation based on whether the participants live with at least one other person or not.

Future studies should take this into account.

Recommendations for Future Research and Practice

Investigating participation by people with disabilities is a complicated endeavor. Previous

research has investigated participation in various activities by various people including

mandatory and discretionary activities, activities that occur inside the home and in the

community, and by mixed groups of disabled people (Card et al., 2006; Chaves et al., 2004;

Crawford, Hollingsworth, Morgan, & Gray, 2008; Gum et al., 2006; Harris, Sprigle, Sonenblum,

& Maurer, 2010; Noreau et al., 2002; Rimmer et al., 2004). This dissertation is among the first to

describe a model of participation that focuses primarily on the effects of psychosocial

functioning of physically disabled people who use wheelchairs and the path diagram of this

model can be found on page 81. Developing this model was, in fact, the intent of this

dissertation. Much greater research will be needed in the future to fill this model out so that it

accounts for the plethora of psychosocial, physical, and environmental factors that influence

participation. Additionally, further research is needed to determine differences and similarities in

patterns of participation by various groups of people with disabilities as well as to determine

their satisfaction with their participation and the facilitators and barriers to participation. It is also

important to compare the findings in this dissertation to research broader populations including

people without disabilities and disabled people who do not use wheelchairs. However, this

dissertation research is cross-sectional in nature. As such, this research yields a limited view of

the role of psychosocial functioning in predicting in community-based, discretionary activities by

wheelchair users. As noted above, each one of the psychosocial factors studied positively

predicted participation when the factors were included in the regression model individually but

most were no longer predictive when analyzed together. In order to understand the relationships

between the psychosocial factors of interest longitudinal studies are needed. Longitudinal studies

are also needed to investigate the predictive value of the medical and demographic covariates

over time.

This research reveals that further investigation regarding wheelchair prescription is

needed. Whether the person used a power wheelchair, manual wheelchair, or scooter was found

not to predict participation in community-based, discretionary activities. The question that arises

from this finding is whether the study participants had the appropriate seating and mobility

devices. Future studies should investigate whether the appropriateness of the mobility device

correlates with participation.

In accordance with the UN Convention on the Rights of People with Disabilities, it may

be beneficial to change the focus of rehabilitation putting greater emphasis on participation in

community-based activities. Because this dissertation research demonstrates that psychosocial

functioning predicts such participation, it may be beneficial to develop inpatient programs that

focus on improving psychosocial functioning, especially general mental health and perceived

control. Such programs could include peer and/or group counseling and assertiveness training.

By addressing general mental health and perceived control, wheelchair users may be more apt to

participate in community-based, discretionary activities after they are discharged from their

rehabilitation hospitals. This research may also indicate that training programs for wheelchair

users should include peer learning. Working with role models who have similar physical

conditions may help people realize how they can increase their control over their own lives

leading to more participation and better satisfaction with their participation in discretionary

activities. Future research in program develop should study whether having novice wheelchair

users work with experienced wheelchair users is an efficient and effective program model.

It would also be beneficial to design follow-up, community-based treatment that allows

individuals to work on skills needed to access the community, to continue to address general

mental health, and to allow individuals to explore various activities that might be of interest. This

represents a major change in traditional practice and would require research to identify the

psychosocial factors and then to determine the best strategies to improve them. If it was

determined that an increased focus on psychosocial functioning in rehabilitation leads to

increased participation in community-based, discretionary activities, it follows that the curricula

of educational programs would need to be adjusted to address these altered focii.

Although this research did not investigate federal, state, or local policy related to

participation by people who use wheelchairs, there are potential implications for policy. The

need for this research was based in part on the principle of full and effective participation and

inclusion of people with disabilities in society by the United Nations Convention of the Rights of

Persons with Disabilities (United Nations General Assembly, 2006). The findings of this

dissertation dovetail with the principles of the Convention in two ways. Firstly, the findings of

this dissertation indicated that perceived control and the perception of reintegration predict

participation in community-based activities. More participation and leadership in the activities of

interest by people who use wheelchairs may increase their own perceived control and

reintegration and may also have a broader effect in that other people who use wheelchairs would

then have more role models of these behaviors.

These findings may have policy implications in that society needs to both be open to

participation and leadership by wheelchair users and make such opportunities available in an

effort to further increase such participation. The second way these findings can have an impact

on policy is to make people think about where they see or do not see people who use

wheelchairs. It may also cause readers to think about why people who use wheelchairs and others

with disabilities are not present more frequently and at various levels in all community-based

activities.

Although this dissertation does not investigate participation in work activities, this is

another area that may be related to this study. In our society, work activities are very important

in defining the individual. According to the Bureau of Labor Statistics, people with disabilities

are employed in the civilian labor force at a rate of 21.8% while the rate for people with no

disabilities is 70.1% (United States Department of Labor, 2012). There may be correlations or

causal relationships between employment and perceived control and reintegration. There may

also be correlations or causal relationships between employment, financial means, and

participation in discretionary activities.

Appendix A: UN Convention of the Rights of Persons with Disabilities: Articles Relevant to this

Research

Article 9- Accessibility. This article addresses accessibility of the physical environment

as well as transportation, information, and communications. It requires that obstacles and barriers

to access be identified and eliminated.

Article 19- Living independently and being included in the community. This article

recognizes the need to live in the community and for people with disabilities to have choices

equal to others for full inclusion and participation in the community.

Article 20- Personal mobility. This article requires that effective measures be taken to

provide the greatest possible independence including affordable mobility aids and training in

their use. This article also instructs equipment producers to address all aspects of mobility.

Article 29- Participation in political and public life. This article recognizes that people

with disabilities have the right to effective and full participation in political and public life be

available to people with disabilities at local, regional, national, and international levels.

Article 30- Participation in cultural life, recreation, leisure, and sport. This article

recognizes that all appropriate measures should be taken to ensure equal access to cultural,

recreational, and leisure activities.

Appendix B: Letter of Agreement

Gray, David [email protected] via nyu.edu

1/16/12

to anita.perr, Gary

Greetings Anita and Hello Dr. Winkel,

I’m pleased that you are interested in the PARTS/M data. The PARTS/M had two versions – a

published version and one with several other activities. The additional activities were eliminated

because of reliability issues. We need to make certain that the data set I sent you is the published

version. Could you attach the version I sent you in a return email. Then I’ll know which data

dictionary to send to you.

The PARTS/M was administered at the same time as the FABS/M. Each survey respondent filled

out a third survey section that has demographics and several other surveys – SF 36, RN, Duke

Social Support Scale, PIP and many of the CDC BRFSS questions. We included these addition

‘standard’ tests to examine the PARTS/M and FABS/M for similarities and differences.

I mention these details to let you know the extent of the data set so that your selection of a topic

for your dissertation considers a variety of possibilities. Please see the attached article for some

details on how we developed the PARTS/M and FABS/M.

I have no problem with your use of the data set for your dissertation. The work you do on the

data set will be yours and yours alone. I would like to be included as an author on papers coming

from your work. But if I have no important intellectual contribution, then you do not need to

include me as an author.

I would like to be a reader on your committee and participate in your committee meeting(s) but I

will need to check the Washington University administration regarding any policy they have that

might prohibit my participation. I seriously doubt they have any such policy. I’ll check

tomorrow.

Cheers,

Dave

http://support.google.com/mail/bin/answer.py?hl=en&ctx=mail&answer=1311182

Appendix C: The Participation Survey/Mobility (PARTS/M)

100

101

102

103

104

105

106

107

108

109

110

111

Appendix D: The Personal Independence Profile (PIP)

Perceived Control Subscale of PIP using a 5-point Likert scale ranging from 1= no control to 5=

complete control.

1. Material comforts

2. Recreation

3. Close friends

4. Health and personal safety

5. Close relationship with significant other

6. Reading, listening to music, etc.

7. Socializing

8. Work in job or at home

9. Relationships with relatives

10. Learning, attending school, etc.

112

Appendix E: The Reintegration to Normal Living Index (RNLI)

The following list includes the statements contained in Return to Normal Living Index. The

index uses a visual analog scale with 1 indicating the lowest and 10 indicating the highest

agreement.

1. I move around my living quarters as I feel is necessary. (Wheelchairs, other equipment or

resources may be used.)

2. I move around my community as I feel is necessary. (Wheelchairs, other equipment or

resources may be used.)

3. I am able to take trips out of town as I feel are necessary. (Wheelchairs, other equipment

or resources may be used.)

4. I am comfortable with how my self-care needs (dressing, feeding, toileting, bathing) are

met. (Adaptive equipment, supervision and/or assistance may be used.)

5. I spend most of my days occupied in a work activity that is necessary or important to me.

(Work activity could be paid employment, housework, volunteer work, school, etc.

Adaptive equipment, supervision and/or assistance may be used.)

6. I am able to participate in recreational activities (hobbies, crafts, sports, reading,

television, games, computers, etc.) as I want to. (Adaptive equipment, supervision and/or

assistance may be used.)

7. I participate in social activities with family, friends, and/or business acquaintances as is

necessary or desirable to me. (Adaptive equipment, supervision and/or assistance may be

used.)

113

8. I assume a role in my family which meets my needs and those of other family members.

(Family means people with whom you live and/or relatives with whom you don’t live but

see on a regular basis. Adaptive equipment, supervision and/or assistance may be used.)

9. In general, I am comfortable with my personal relationships.

10. In general, I am comfortable with myself when I am in the company of others.

11. I feel that I can deal with life events as they happen.

114

Appendix F: The Medical Outcomes Study 36-Item Short Form Health Survey (SF-36)

115

116

117

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