Mobility Device Quality Affects Participation Outcomes for People With Disabilities: A Structural Equation Modeling Analysis

Accepted Manuscript Mobility device quality impacts participation outcomes among people with disabilities: A structural equation modeling analysis Susan Magasi, Alex W.K. Wong, Ana Miskovic, David Tulsky, Allen W. Heinemann PII:

S0003-9993(17)30526-9

DOI:

10.1016/j.apmr.2017.06.030

Reference:

YAPMR 56973

To appear in:

ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION

Received Date: 10 January 2017 Revised Date:

16 May 2017

Accepted Date: 30 June 2017

Please cite this article as: Magasi S, Wong AWK, Miskovic A, Tulsky D, Heinemann AW, Mobility device quality impacts participation outcomes among people with disabilities: A structural equation modeling analysis, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2017), doi: 10.1016/ j.apmr.2017.06.030. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Running Header: Mobility device quality impacts participation Title: Mobility device quality impacts participation outcomes among people with disabilities: A structural equation modeling analysis

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Susan Magasi1, Alex W.K. Wong2, Ana Miskovic3, David Tulsky4, Allen W. Heinemann3,5 Affiliations 1

1919 W. Taylor, Rm. 327, Chicago, IL 60612 2

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University of Illinois at Chicago, Departments of Occupational Therapist and Disability Studies,

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Washington University, School of Medicine, Departments of Occupational Therapy and

Neurology, 4444 Forest Park Ave, St. Louis, MO 63108-1651 3

Shirley Ryan Ability Lab, 355 E. Erie St., Chicago, IL 60610

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University of Delaware, Center on Assessment Research and Translation and Departments of

Physical Therapy and Psychological and Brain Sciences, 101 Discovery Blvd, Newark, DE 19713 5

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Northwestern University, Department of Physical Medicine and Rehabilitation, Shirley Ryan

Ability Lab, Center for Rehabilitation Outcomes Research, 355 E. Erie St., Chicago, IL 60610

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Conflict of Interest

The authors declare no conflicts of interest

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Acknowledgements

This research was supported by a Rehabilitation Research and Training Center Grant from the National Institute on Disability, Independent Living, and Rehabilitation Research (H133B090024).

Corresponding Author: Susan Magasi

ACCEPTED MANUSCRIPT Running Header: Mobility device quality impacts participation University of Illinois at Chicago, Departments of Occupational Therapy and Disability Studies

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1919 W. Taylor, Rm. 327, Chicago, IL 60612; [email protected] ; 773-551-4702

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Abstract

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Objective: To test the effect that indicators of mobility device quality have on participation

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outcomes among community dwelling adults with spinal cord injuries (SCI), traumatic brain

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injuries (TBI) and stroke using structural equation modeling.

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Design: Survey, cross-sectional study, and model testing.

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Setting: Clinical research space at 2 academic medical centers and one free-standing

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rehabilitation hospital in the Midwestern United States (St. Louis, Ann Arbor, Chicago).

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Participants: Community-dwelling adults (mean age= 48 years(SD 14.3)) with SCI, TBI and

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Stroke (n=250).

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Interventions: Not applicable

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Main Outcomes Measures: The Mobility Device Impact Scale, PROMIS Social Health (v2.0)

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questionnaires, including Ability to Participate in Social Roles and Activities and Satisfaction

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with Social Roles and Activities questionnaires and the 2 Community Participation Indicators’

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Enfranchisement Scales. Details about device quality (reparability, reliability, ease of

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maintenance) and device type were also collected.

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Results: Respondents used ambulation aids (30%), manual (34%), and power wheelchairs

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(30%). Indicators of device quality had a moderating association with participation outcomes,

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with three device quality variables, ease of repairs and maintenance, and device reliability

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accounting for 20% of the variance in participation. Wheelchair users reported lower

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participation enfranchisement than persons using ambulation aids.

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Conclusion: Mobility device quality plays an important role in participation outcomes. It is

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critical that people have access to mobility devices and that these devices be reliable.

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Key Words: Wheelchairs; Walkers; Stroke; Spinal cord injuries; Brain Injuries, Traumatic;

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Outcome and Process Assessment (Health Care); Social participation

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CFO – Confirmatory Factor Analysis

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CFI – Comparative Fit Index

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CR – Critical Ratio

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EFIB – Environmental Factors Item Banks

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PRO – Patient Reported Outcome

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RMSEA – Root Mean Square Error of Approximation

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SCI – Spinal Cord Injury

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SEM – Structural Equation Model

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SRA – Social Roles and Activities

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TBI – Traumatic Brain Injury

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TLI – Tucker Lewis Index

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Acronyms and Abbreviations

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Running Header: Mobility Device Quality Impact Participation

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Introduction

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There are approximately 9.4 million mobility device users in the United States, including 3.3 million users of wheeled mobility devices, such as manual and power wheelchairs and

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electric scooters1, and 6.1 million users of ambulation aids, including canes, crutches and

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walkers.2 Wheeled mobility devices are complex pieces of equipment designed to compensate

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for decreased ambulatory abilities and to provide increased functional mobility at home and in

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the community. Ambulation aids promote independent ambulation by widening the base of

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support, decreasing weight bearing on the lower extremities, and providing sensory and

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proprioceptive feedback regarding body position and changes in walking surface.3 There is a

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great deal of variability in the complexity and cost of mobility devices from a simple straight

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cane available at the corner store to a custom power wheelchairs that require skilled specialty

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services to prescribe, fit and maintain. The choice of mobility device is based on an individual’s

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needs and resources. Different types of mobility devices have differential effects on functional

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outcomes.4

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Mobility devices also allow people to exercise their human rights and achieve inclusion

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and equality in participation.5 The United Nations’ Standard Rules for the Equalization of

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Opportunities for Persons with Disabilities,6 the Convention on the Rights of Persons with

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Disabilities,7 and the 58th World Health Assembly8 all highlight the importance of mobility

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devices to promote independence and participation for people with disabilities. Article 20 of

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the United Nations’ Convention on the Rights of Persons with Disabilities enshrines access to

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quality mobility devices as a fundamental human right.7

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While the relationship between mobility device use and positive health and participation outcomes is well-supported in the literature;9-13 there is a growing body of

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research that demonstrates wheelchair breakdowns contribute to participation restrictions

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among people with spinal cord injuries (SCI).14-16 For example, Worobey and colleagues found

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that 47.6% to 63.3% of power wheelchair users with SCI experienced breakdowns in the past 6-

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months and that breakdowns led to being stranded, injured or restricted in work and school

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participation, and healthcare access.15 Toro and colleagues found that when wheelchair users

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with SCI experienced breakdowns only 40% of manual wheelchair users and 17% of power chair

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users were able to fix their devices at home with the majority of people requiring specialty

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repair services.14 The time required to repair a mobility device can further restrict participation.

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Frequency of breakdowns increase with age of the mobility device.17 According to

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reimbursement policies in the United States, mobility devices are expected to last 3-5 years.

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Scrutiny of public spending for durable medical equipment, including mobility devices, has led

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to Medicare regulations aimed at containing these costs.18,19 Restrictive policies for the

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provision, repair and replacement of mobility devices may have unintended consequences of

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restricting access to the people who need them the most and limit their opportunities for

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community participation.20

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A better understanding of the relationship between device quality and participation

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outcomes can provide guidance to policy makers, funders, and clinicians about the salience of

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device quality to public health outcomes, such as community participation. To our knowledge,

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no other study has examined the relationship between indicators of device quality and

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participation outcomes in a cross disability sample of mobility device users. Thus, the purpose

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of this study is to examine the associations between device type and perceived quality on

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participation outcomes. Conceptual Underpinnings

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Mobility Devices

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Extensive qualitative research with people with disabilities indicates that mobility devices are

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both facilitators and barriers to participation.21,22 Mobility devices must meet users’ needs, function

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reliably and be useful across a variety of contexts and physical environments. For the purpose of this

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analysis, we considered 3 aspects of mobility devices – the type (ambulation aid versus wheeled mobility

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device), the quality, and the impact on participation.

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Participation

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The International Classification of Functioning, Disability and Health’s definition of participation as “involvement in life situations”23 is deceptively simple. Participation is a complex, multifaceted

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construct that scientists have struggled to operationalize.24,25 Participation is a person-centered concept

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that can be understand as a person’s ability to participate and their satisfaction with participation as

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well as their sense of participation enfranchisement.26 Participation enfranchisement is a new concept

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that reflects an individual’s appraisal of the importance of and control over participation.26,27

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Figure 1 illustrates the relationships we hypothesized between device quality and

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participation. We defined five indicators of device quality (reliable, easy to maintain, repairable,

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replaceable, portable). In turn, we expected that these indicators along with device type

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(ambulation aids, manual, power wheelchair) would influence device quality with more

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complex mobility devices being more susceptible to breakdowns and lower user ratings of

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quality. We expected that the effect of device quality would have a moderating effect on

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participation with higher device quality associated with better participation outcomes. We also

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expected that device type would have a mediating effect on participation outcomes. A

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moderating variable is one that influences the strength of a relationship while a mediator

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explains the relationship between the two other variables.

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Methods

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As part of a larger study, 604 community-dwelling adults with SCI, traumatic brain

injuries (TBI) and stroke participated in a comprehensive, 2-day assessment. SCI, TBI and stroke

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are among the most prevalent causes of adult onset physical disability requiring medical rehabilitation.

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In 2016, SCI, TBI and stroke accounted for over one third of all in-patient rehabilitation cases 28. While

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people often make significant improvements during inpatient rehabilitation, many people with SCI, TBI

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and stroke are discharged to community settings with long-term physical disabilities that require use of

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mobility devices.

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We recruited participants via community outreach networks, registries, and flyers. Interested individuals contacted the research team and were screened for eligibility. Eligibility

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criteria included: community residence; traumatic SCI, TBI, or stroke; at least one-year following

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their most recent injury; 18 to 85 years of age; ability to read at a fifth grade level; and ability to

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speak English. Medical documentation was required to confirm participants’ injuries. This study

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received ethics approval from the Institutional Review Boards at each of the three collaborating

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sites and all participants provided informed written informed consent. Only participants who

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self-identified as mobility device users and completed the related study measures were

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included in this analysis. Participants received an honorarium to acknowledge their

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contribution to the research.

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Data collection Testing occurred over 2 days (lasting an average of 4.9 and 4.7 hours, respectively) in clinical research space at 2 academic medical centers and one free-standing rehabilitation

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hospital in the Midwestern United States (St. Louis, Ann Arbor, Chicago). Patient-reported

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outcome (PRO) measures of mobility device use and participation were computer administered

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using Assessment Center® under the guidance of a certified test administrator on the second

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day. Data were collected between December 2011 and November 2013.

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Measures

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Mobility Device Use

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Device Type–As part of the larger testing protocol, participants indicated if they used a mobility

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device and then specified the device that they use most often. We categorized device type as

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ambulation aids, manual wheelchairs, or power mobility devices. A clinical expert in seating and positioning confirmed device classification. Participants who self-identified as users of mobility devices

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completed items rating the quality of their mobility device.

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Device Quality– Based on extensive qualitative research with people with disabilities on the participation and environmental factors21,29 and a review of the literature, we developed 5-indicators of

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mobility device quality, including reliability, ease of maintenance, ease of repairs, replaceability, and

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portability. Each item was scored using a 5-point agreement scale that ranges from very much (5) to not

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at all (1). Device quality items were entered into the structural equation model as latent variables.

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Mobility Device Impact - Mobility Device Impact Scale was developed as part of the

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Environmental Factors Item Banks (EFIB), 22 using the PROMIS methodology.30 The Mobility Device

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Impact Scale is a 5-item scale designed to measure the perceived impact that a mobility device has on

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participation outcomes. It is scored on a 5-point agreement scale from agree strongly (5) to disagree

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acceptable; infit and outfit MnSq values were between .75 and 1.33 an acceptable range31 and infit and

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outfit Zstd values were all below a critical value of 2.32 Internal consistency is excellent (Cronbach alpha

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= 0.92). See Table 1 for device quality items and Mobility Device Impact Scale.

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Participation Measures

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Enfranchisement Scale to measure aspects of participation.

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We used the PROMIS Social Health (v2.0) and the Community Participation Indicators (CPI)

PROMIS Social Function (v2.0) is comprised of two measures: Ability to Participate in Social Roles

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and Activities, and Satisfaction with Social Roles and Activities,33 which demonstrate strong

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psychometric properties including reliability and validity, and no evidence of differential item

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functioning by gender, age, education or language.33,34 The PROMIS method allows comparability across

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populations because it measures common, generic experiences that apply to people with a variety of

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conditions.35

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CPI Enfranchisement Measure is comprised of 2 scales: importance of participation and control over participation,21,26,27,36 which demonstrate strong psychometric properties among adults with

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disabilities including validity and reliability. Rasch analysis of both scales was favorable, with the

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Importance and Control Scales demonstrating good person separation (2.66 and 2.28, respectively) and

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excellent item separation (15.50 and 14.81, respectively).26

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Analysis

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We used descriptive statistics to characterize the sample. We assessed distribution

normality by inspecting histograms and examining skewness and kurtosis values. The critical

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values of skewness and kurtosis were <2.0 and <7.0, respectively, to indicate that the normality

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assumption was not violated.37 We used structural equation modeling (SEM) to examine the relationships between

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latent traits related to device quality and impact and the more distal outcomes of social

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participation and isolation. We tested model fit to the data in several steps.38,39 First, we

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completed confirmatory factor analysis (CFA) to assure that the measurement model is valid.

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We allowed latent variables to covary without specifying structural relations, and restricting

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manifest variables to load onto the corresponding latent variables. Subsequently, we used path

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modeling to examine the relationships between the latent constructs and variables. The models

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exclude cases with missing data. In both steps, we estimated parameters using maximum

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likelihood methods. We examined fit statistics and standardized factor loadings with each

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model to assess model fit and convergent validity.

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Multiple fit indices assess how well the models fit the data. The χ2 statistic is the

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strictest form of SEM model-testing 38,39 as it is sensitive to large samples. Thus, we used

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alternative indices of model fit. The Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) are

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measures of incremental model fit. Values > 0.90 are criteria for acceptable model fit and >0.95

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for good model fit.40 The Root Mean Square Error of Approximation (RMSEA) represents

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closeness of fit; values< 0.08 and < 0.06 indicate acceptable and good fit, respectively.41 We also

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examined group invariance in the final model across impairment, gender, marital status, race,

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age, and education.42 We compared an unconstrained model where a set of parameters were

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allowed to vary across the groups (e.g., males vs. females) with a constrained model where all

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estimated parameters were constrained to be equal. We computed chi-square differences to

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determine whether constraining the parameters reduced the model fit. We completed analyses

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with SPSSa and AMOS 21.0b software. Results

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Descriptive Statistics

Table 2 provides an overview of participant demographics; 250 people (41% of the

sample) self-identified as mobility device users. While participants in the parent study were

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evenly distributed across the three diagnostic groups, mobility device use varied such that

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people with SCI were more likely to use mobility devices, followed by people with stroke and

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TBI. Of these device users, 31% (n=77) used an ambulation aid, 36% (n=90) used a manual

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wheelchair, and 33% (n=83) used a power device.

All variables included in the SEM analysis were below the critical values of skewness (2.0) and kurtosis (7.0) distribution.

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Structural Equation Models

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We created a dichotomous variable to reflect device use, distinguishing wheeled mobility from ambulation aids. The measurement model provided acceptable fit to the data

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(CFA=0.975; TLI=0.959; RMSEA=0.055 with 90% CI, 0.024-0.083). Table 3 shows standardized

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factor loadings of manifest variables for the latent constructs. The critical ratio and significant p

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values provide evidence to support the convergent validity of the indicators. CFA indicated the

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latent constructs of participation and device quality were measured adequately by their

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respective manifest variables.

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Fit indices (CFA=0.941; TLI=0.916; RMSEA=0.079 with 90% CI, 0.055-0.103) for the initial conceptual model (Figure 1) suggest that the fit was less than adequate. We made post hoc

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modification to create a better fitting model. Modifications include removing non-significant

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paths and adding paths based on the modification index (MI).38,39 MI values suggested that we

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should add a direct path between device type and device benefits to achieve better fit. Prior to

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accepting quantitatively informed suggestions, we evaluated the extent to which the revised

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model was conceptually, clinically, and theoretically informed.

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The final model (Figure 2) indicates relatively good fit to the data (CFA=0.960; TLI=0.940;

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RMSEA=0.067 with 90% CI, 0.041-0.092). All coefficients were statistically significant

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(p<0.001).Device quality was predicted by device type (β=-0.28) such that wheelchair users

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reported lower device quality than did ambulation aid users. Device type had direct (β=0.232)

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and indirect effects via device quality (β=0.404) on device benefits. Device benefits contributed

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positively to participation(β=0.435). The R2 values show that 8% of the variance in device

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quality was accounted for by device type, 16% of the variance in device benefits was accounted

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for by device quality and device type, and 19% of the variance in participation was accounted

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for by device benefits. We ran correlation matrix among indicators in our model and found that

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none of the correlations between indicators were above r=0.85, suggesting no collinearity

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issues in our model.

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Testing Clinical Moderators

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The final model was invariant across gender, marital status, race, age, and education,

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suggesting that the final model is valid for men and women, married and non-married

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participants, people who are older or younger, and people with high and low education (Table

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4). Factor loading of ease of maintenance and factor variance of device type differed by

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impairment group. After accounting for the measurement model variation, the path structure

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did not differ, indicating that the path relationships in the final model are valid regardless of

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primary impairment (spinal injuries vs. brain injuries).

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Discussion

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This study enhances our understanding of how mobility device quality influences

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participation outcomes by modeling the mediating role that individuals’ appraisals of device

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reliability, ease of maintenance, and reparability have on participation outcomes. Device type,

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which may serve as a proxy for functional mobility4, has a moderating role on participation

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outcomes. We defined device quality as reflecting perceptions of maintenance issues, repair

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needs, and reliability. Ambulation aid users perceived their mobility devices as demonstrating

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better quality than did wheelchair users. We hypothesized portability and replaceability as

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markers of device quality, but this hypothesis was not supported. Upon reconsidering the

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conceptual model, it may be that portability is better understood as the interaction between

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mobility device and the receptivity of the physical environments and transportation systems.

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Similarly, while poor device quality may necessitate replacement, the ability to replace a

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mobility device is determined by one’s resources and the systems, services and policies that

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govern the provision of durable medical equipment.

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Both device quality and device type influenced perceived device benefits such that

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respondents perceived that better quality mobility devices provided greater benefits. We

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improved model fit by modeling device benefits as a function of device quality and device type.

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Users of ambulation devices rated the quality of their mobility device better than did users of

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wheeled mobility devices, wheeled mobility device users, especially those who rated their

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devices higher on quality perceived greater device benefits. We modeled participation as reflecting control over participation, involvement in life

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situations, ability to participate in social roles and activities, and satisfaction with social roles

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and activities. In turn, persons who reported greater device benefits were more likely to report

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greater levels of participation. While the standardized regression coefficient (0.435) is

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moderate in magnitude, this coefficient indicates that device benefits influence participation to

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a substantial degree. Participation likely reflects other environmental and personal factors;

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nonetheless, the findings illustrate that quality of mobility devices accounts for variation in

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participation.

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This study contributes to the growing body of evidence that poor quality of mobility devices can contribute to participation restrictions.14-16 These studies support the relationship between

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breakdowns, arguably a marker of poor quality, and selected participation outcomes in wheelchair users

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with SCI. The current study expands this research to a cross disability sample of mobility device users

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and includes consideration of a broader range of mobility devices. Furthermore, our findings indicate

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that it is not only breakdowns that lead to participation restrictions, but that users’ perceptions of their

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mobility device’s reliability, reparability and ease of maintenance also contribute to participation

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outcomes.

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In an effort to contain costs, Medicare’s policies have instituted competitive bidding for the

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provision of mobility devices in many areas across the United States.43 People with disabilities and

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professional organizations, such as the Rehabilitation Engineering and Assistive Technology Society of

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North America (RESNA), have raised concerns that competitive bidding for the provision and long-term

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maintenance of mobility devices emphasize cost savings over quality and may have the unintended 14

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here regarding device quality’s effects on participation can inform policies and consider the needs of

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people with disabilities.

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Gender, marital status, race, age, education, and impairment type were not related to the direction or strength of relationships between device type, quality, benefits, and

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participation. While these findings are encouraging, replication in other regions and with

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samples that have more diverse payment sources are needed to gain confidence that there are

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few disparities due to these characteristics.

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Implications for Future Research

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The impact of mobility device quality is best understood within its context of use, including the built, physical, social and economic environment. Future research should

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examine the ways in which participation outcomes and environmental factors are related, while

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taking into consideration a broader set of environmental factors to explore the interactional

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relationships between environmental factors and how they influence public health outcomes

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for people with disabilities.

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Study Strengths and Limitations

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Strengths of this study include a focus on community dwelling adults who have extensive

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experience using their device, reflecting the “real world” ways that people use their mobility

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devices. While 43% of the cohort in the larger study from which our sample was drawn

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identified as mobility device users, this was not an inclusion criterion, and mobility device users

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were not targeted for recruitment. The sample was skewed towards positive appraisals of

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devices and, as a result, some aspects of device quality such as appropriateness for their needs

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and ease of use were not included in our analysis. Furthermore, the 2-day testing protocol may

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have contributed to a selection bias towards people who were able to travel and participate

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and may limit generalizability of the study results. Participants only provided ratings of the

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devices they used most often and study results may not reflect the experience of people who

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use multiple mobility devices. Furthermore while the findings support the provision of reliable

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mobility devices, 80% of the variance in participation was not explained. This finding highlights

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the complexity of the inter-relationship between participation outcomes and environmental

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factors.24,25,45 The contributions of other factors in the built, social, economic and policy

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environment must also be considered to gain a more complete understanding participation

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outcomes among mobility device users.

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Future studies of device quality, device type and participation outcomes should target

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mobility device users to engage a broader spectrum of users including those who are satisfied

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with their devices and those who are experiencing challenges. Future studies should distinguish

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users of manual wheelchairs and power mobility devices to understand how device complexity

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influences service and maintenance needs as well as participation. Determining the extent to

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which this finding reflects idiosyncrasies of this sample requires validation with a new sample.10

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Conclusion

Increasing participation by people with disabilities is the goal of public policy and

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rehabilitation. Mobility devices help improve personal mobility and allow people with

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disabilities to exert their rights for full and equal participation in society. Insurer policies dictate

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how often people can replace their wheelchairs and under what circumstances repairs and

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maintenance are funded. The quality of mobility devices (including reliability, reparability, and 16

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ease of maintenance) has an important moderating effect on participation outcomes. These

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findings inform policy makers, including those who set policies about wheelchair provision,

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replacement and repair about the impact that these funding decisions have on the participation

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of people with disabilities. Findings can also be used by providers of mobility devices to support

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the justification of skilled services to ensure that mobility device users receive timely services to

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maintain, repair, and replace devices in order to promote participation.

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References

6. 7. 8. 9.

10.

11.

12.

13.

14.

15.

16.

17. 18.

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5.

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LaPlante MP, Kaye HS. Demographics and trends in wheeled mobility equipment use and accessibility in the community. Assistive Technology®. 2010;22(1):3-17. Kaye HS, Kang T, LaPlante MP. Mobility Device Use in the United States. Disability Statistics Report 14. 2000. Aminzadeh F, Edwards N. Factors associated with cane use among community dwelling older adults. Public Health Nursing. 2000;17(6):474-483. Jutai J, Coulson S, Teasell R, et al. Mobility assistive device utilization in a prospective study of patients with first-ever stroke. Archives of Physical Medicine and Rehabilitation. 2007;88(10):1268-1275. World Health Organization. Guidelines on the provision of manual wheelchairs in less resourced settings. 2008. World Health Organization. United Nations Standard Rules for the Equalization of Opportunities for Persons with Disabilities. Geneva; United Nations;1982. United Nations. Convention on the Rights of Persons with Disabilities - Articles. Geneva: United Nations; 2008. 58th World Health Assembly. Disability, including prevention, management and rehabilitation. Geneva: World Health Organization; 2005. Salminen AL, Brandt A, Samuelsson K, Toytari O, Malmivaara A. Mobility devices to promote activity and participation: a systematic review. Journal of Rehabilitation Medicine. 2009;41(9):697-706. Pettersson I, Hagberg L, Fredriksson C, Hermansson LN. The effect of powered scooters on activity, participation and quality of life in elderly users. Disability and Rehabilitation. Assistive Technology. 24 2015:1-6. Brandt A, Lofqvist C, Jonsdottir I, et al. Towards an instrument targeting mobility-related participation: Nordic cross-national reliability. Journal of Rehabilitation Medicine. 2008;40(9):766-772. Sund T, Iwarsson S, Anttila H, Brandt A. Effectiveness of Powered Mobility Devices in Enabling Community Mobility-Related Participation: A Prospective Study Among People With Mobility Restrictions. PM & R : the journal of injury, function, and rehabilitation. 2015;7(8):859-870. Clarke PJ. The role of the built environment and assistive devices for outdoor mobility in later life. The journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 2014;69 Suppl 1:S8-15. Toro ML, Worobey L, Boninger ML, Cooper RA, Pearlman J. Type and frequency of reported wheelchair repairs and related adverse consequences among people with spinal cord injury. Archives of Physical Medicine and Rehabilitation. 2016;97(10):1753-1760. Worobey L, Oyster M, Nemunaitis G, Cooper R, Boninger ML. Increases in Wheelchair Breakdowns, Repairs, and Adverse Consequences for People with Traumatic Spinal Cord Injury. American Journal of Physical Medicine & Rehabilitation. 2012;91(6):463-469. Worobey L, Oyster M, Pearlman J, Gebrosky B, Boninger ML. Differences between manufacturers in reported power wheelchair repairs and adverse consequences among people with spinal cord injury. Archives of Physical Medicine and Rehabilitation. 2014;95(4):597-603. Wang H, Liu H-Y, Pearlman J, et al. Relationship between wheelchair durability and wheelchair type and years of test. Disability and Rehabilitation: Assistive Technology. 2010;5(5):318-322. Mayer S. Those Scamming Little Rascals: Power Wheelchair Fraud and the Flaw in the Medicare System. DePaul Journal of Health Care Law. 2015;17:149.

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24.

25.

26.

27. 28. 29.

30. 31. 32. 33. 34.

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36.

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Reschovsky JD, Ghosh A, Stewart KA, Chollet DJ. Durable medical equipment and home health among the largest contributors to area variations in use of Medicare services. Health Affairs. 2012;31(5):956-964. Pedersen JP, Harmon D, Kirschner KL. Is an appropriate wheelchair becoming out of reach? PM&R. 2014;6(7):643-649. Hammel J, Magasi S, Heinemann A, Whiteneck G, Bogner J, Rodriguez E. What does participation mean? An insider perspective from people with disabilities. Disability and Rehabilitation. 2008;30(19):1445-1460. Heinemann AW, Magasi S, Hammel J, et al. Environmental Factors Item Development for Persons With Stroke, Traumatic Brain Injury, and Spinal Cord Injury. Archives of Physical Medicine and Rehabilitation. 2015;96(4):589-595. World Health Organization. International Classification of Functioning, Disability and Health. Geneva. World Health Organization. 2001:1-303. Whiteneck G, Dijkers MP. Difficult to measure constructs: conceptual and methodological issues concerning participation and environmental factors. Archives of Physical Medicine and Rehabilitation. 2009;90(Suppl 11):S22-35. Badley EM. Enhancing the conceptual clarity of the activity and participation components of the International Classification of Functioning, Disability, and Health. Social Science & Medicine. 2008;66(11):2335-2345. Heinemann AW, Magasi S, Bode RK, et al. Measuring Enfranchisement: Importance of and Control Over Participation by People With Disabilities. Archives of Physical Medicine and Rehabilitation. 2013;94(11):2157-2165. Heinemann AW, Lai J-S, Magasi S, et al. Measuring participation enfranchisement. Archives of Physical Medicine and Rehabilitation. 2011;92(4):564-571. MedPAC. Report to Congress: Medicare Payment Policy, March 2016. Washington, DC: MedPAC;2016. Hammel J, Magasi S, Heinemann A, et al. Environmental Barriers and Supports to Everyday Participation: A Qualitative Insider Perspective From People With Disabilities. Archives of Physical Medicine and Rehabilitation. 2015;96(4):578-588. DeWalt DA, Rothrock N, Yount S, Stone AA, Group PC. Evaluation of item candidates: the PROMIS qualitative item review. Medical Care. 2007;45(5 Suppl 1):S12-21. Wilson M. Constructing measures: An item response modeling approach. Routledge; 2004. Smith Jr EV, Smith RM. Introduction to Rasch Measurement: Theory, Models and Applications. Jam Press; 2004. Hahn EA, DeWalt DA, Bode RK, et al. New English and Spanish social health measures will facilitate evaluating health determinants. Health Psychology. 2014;33(5):490. Hahn EA, Kallen MA, Jensen RE, et al. Measuring Social Function in Diverse Cancer Populations: Evaluation of Measurement Equivalence of the Patient Reported Outcomes Measurement Information System®(PROMIS®) Ability to Participate in Social Roles and Activities Short Form. Psychological Test and Assessment Modeling. 2016;58(2):403. Cella D, Riley W, Stone A, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. Journal of Clinical Epidemiology. 2010;63(11):1179-1194. Magasi S, Hammel J, Heinemann A, Whiteneck G, Bogner J. Participation: a comparative analysis of multiple rehabilitation stakeholders' perspectives. Journal of Rehabilitation Medicine. 2009;41(11):936-944. Curran PJ, West SG, Finch JF. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods. 1996;1(1):16.

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Kline R. Principles and Practice of Structural Equation Modeling. Guilford Press. New York. 2005:59. Weston R, Gore Jr PA, Chan F, Catalano D. An introduction to using structural equation models in rehabilitation psychology. Rehabilitation Psychology. 2008;53(3):340-356. Browne MW, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JA, eds. Testing Structural Equation Models. Sage Publications: Thousand Oaks; 1993:136-162. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6(1):1-55. Byrne BM. Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural Equation Modeling. 2004;11(2):272-300. Medicare.gov. What's the competitive bidding program? https://www.medicare.gov/whatmedicare-covers/part-b/competitive-bidding-program.html. Accessed 4/30/2017. Rehabiltation Engineering and Assistive Technology Society of North American (RESNA). RESNA Policy Position Statement. 2015. Magasi S, Wong A, Gray DB, et al. Theoretical Foundations for the Measurement of Environmental Factors and Their Impact on Participation Among People With Disabilities. Archives of Physical Medicine and Rehabilitation. 2015;96(4):569-577.

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Figure Legend

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Figure 1 – Initial Conceptual Model

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Figure 2 – Final Model

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Table 1. Mobility Device Items Construct

My mobility device is reliable

Reliability

My mobility device is easy to maintain

Ease of Maintenance

My mobility device can be easily repaired

Reparability

I can replace my mobility device when I need to

Replaceability

I can use my mobility device in a variety of places

Portability

SC

RI PT

Device Quality Items

Device Impact Items

M AN U

My mobility device gives me more control over my daily activities My mobility device helps me be more independent

My mobility device helps me feel more confident when doing my daily activities My mobility device allows me to participate in activities that I enjoy

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My mobility device helps me achieve my goals

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Table 2. Demographic Characteristics Total (N=250)

SCI (N=165)

TBI (N=17)

Stroke (N=68)

Mean (SD) Years Since Injury Mean (SD)

48 (14.3) N=249 10 (9.9)

45 (14.0)

43 (11.4)

13 (10.4)

10 (8.7)

58 (11.0) N=67 3 (3.5)

Gender Male Female

179 (72%) 71 (28%)

133 (81%) 32 (19%)

10 (59%) 7 (41%)

42 (55%) 35 (45%)

N=249

N=164

22 (9%) 123 (49%) 96 (39%) 8 (3%)

16 (10%) 99 (61%) 45 (27%) 4 (2%)

3 (18%) 5 (29%) 9 (53%) -

3 (4%) 19 (28%) 42 (62%) 4 (6%)

Employment Yes No

EP

Personal Income $0 - $14,999 $15,000 - $34,999 $35,000 - $54,999 $55,000 - $74,999 $75,000 or more

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Household Income $0 - $14,999 $15,000 - $34,999 $35,000 - $54,999 $55,000 - $74,999 $75,000 or more

SC

77 (31%)

18 (11%)

9 (53%)

50 (74%)

173 (69%)

147 (89%)

8 (47%)

18 (26%)

N=256

N=164

N=17

N=76

47 (19%) 202 (81%)

41 (25%) 123 (75%)

2 (12%) 15 (88%)

4 (6%) 64 (94%)

N=206

N=137

N=17

N=55

96 (47%) 71 (35%) 17 (8%) 5 (2%) 17 (8%)

58 (42%) 52 (38%) 13 (10%) 1 (1%) 13 (9%)

9 (56%) 5 (31%) 0 (0%) 0 (0%) 2 (13%)

30 (55%) 15 (27%) 4 (7%) 4 (7%) 2 (4%)

N=186

N=124

N=17

N=52

52 (28%) 45 (24%) 30 (16%) 19 (10%) 40 (22%)

30 (24%) 30 (24%) 18 (15%) 14 (11%) 32 (26%)

4 (40%) 2 (20%) 2 (20%) 0 (0%) 2 (20%)

18 (35%) 13 (25%) 10 (19%) 5 (10%) 6 (11%)

TE D

Mobility Device Ambulation Aid (Cane, Crutch, Walker) Wheeled Mobility (Manual Wheelchair, Power chair, Scooter)

M AN U

Ethnicity/Race Hispanic any race Non-Hispanic White Non-Hispanic Black Non-Hispanic Other

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Age

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Table 3. Standardized Factor Loadings of Participation and Device Quality Maximum Likelihood Estimation Regression Coefficient

SE

0.682 0.805 0.761

1 0.751 0.621

0.078 0.065

0.692 0.450 0.710 0.805

1 0.606 0.949 1.333

CR

p-value

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0.077 0.105 0.144

M AN U

Device Quality Repair Maintain Reliable Participation Control Involvement Ability to participate in SRA Satisfaction with SRA

Standardized Regression Coefficient

SC

Construct and indicators

9.673 9.542

7.832 9.109 9.450

<0.001 <0.001

<0.001 <0.001 <0.001

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NOTE: SE = Standardized Error; CR = Critical Ratio; SRA = Social Roles and Activities; CR values for estimates of “Repair” and “Control” were not reported as their factor loadings were fixed to 1 and not estimated to scale the latent constructs.

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Table 4: Fit statistics for tests of group invariance Model

Gender: male (n=179) vs female (n=71) Unconstrained model (M8) Constrained model with all parameters constrained equal (M9)

TE D

Marital status: married/partner (n=81) vs non-married/non-partner (n=154) Unconstrained model (M10) Constrained model with all parameters constrained equal (M11)

EP

Race: white (n=123) vs non-white (n=126) Unconstrained model (M12) Constrained model with all parameters constrained equal (M13)

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Age: younger (<50 years old; n=124) vs older (> 50 years old; n=126) Unconstrained model (M14) Constrained model with all parameters constrained equal (M15) Education level: < high school (n=86) vs > high school (n=164) Unconstrained model (M16) Constrained model with all parameters constrained equal (M17) Abbreviation: NS, not significant

RI PT

Δdf

p-value

50 60 55 53 54 55 58

44.132 17.964 4.377 4.762 22.805 10.559

10 5 3 4 5 8

<0.001 <0.01 NS NS <0.001 NS

74.557 91.036

48 58

16.479

10

NS

74.419 84.201

48 58

9.782

10

NS

104.78 114.296

48 58

9.516

10

NS

73.289 87.046

48 58

13.757

10

NS

72.447 77.583

48 58

5.136

10

NS

SC

67.438 111.57 85.402 71.815 72.2 90.243 77.997

M AN U

Impairment group: SCI (n=165) vs TBI & stroke (n=85) Unconstrained model (M1) Constrained model with all parameters constrained equal (M2) Constrained model with all factor loadings constrained equal (M3) Constrained model with factor loadings of participation constrained equal (M4) M4 and factor loading of reliability constrained equal (M5) M5 and factor variance of device type constrained equal (M6) M6 and all structural paths constrained equal (M7)

Δχ2

df

χ2

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Figure 1. Initial Conceptual Model Reliable

RI PT

Control

Device Quality

Involvement Participation Ability to participate in SRA

AC C

EP

Replaceable

Portable

Device Benefits

TE D

Repairable

M AN U

SC

Maintain

Device Type

Satisfaction with SRA

Note: Errors in indicators not accounted for by a latent variable and errors in dependent variables not accounted for by predictors are omitted from this figure. Solid lines represented regression coefficients; dotted lines represent factor loadings.

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Figure 2. Final Model

Maintain

0.805**

Device Quality

R-squared=8%

-0.280**

TE D

R-squared=16%

Device Type

EP

0.232**

AC C

Repair

0.682†

Device Benefits

0.404**

0.692† Involvement

SC

0.761**

M AN U

Reliable

RI PT

Control

0.435**

0.450** Participation R-squared=19%

0.710**

0.809**

Ability to participate in SRA

Satisfaction with SRA

Note: Errors in indicators not accounted for by a latent variable and errors in dependent variables not accounted for by predictors are omitted from this figure. Solid lines represented regression coefficients; dotted lines represent factor loadings. Standardized coefficients estimated by ML are presented. **p<0.001, †No p-value was reported because one factor loading for a latent variable was fixed at 1.0 and not estimated to scale the latent variable. Replicable and portable were dropped from the final model because these two indicators were not significantly loaded into the device quality.

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Figure 2. Final Model

Maintain

0.805**

Device Quality

R-squared=8%

-0.280**

TE D

R-squared=16%

Device Type

EP

0.232**

AC C

Repair

0.682†

Device Benefits

0.404**

0.692† Involvement

SC

0.761**

M AN U

Reliable

RI PT

Control

0.435**

0.450** Participation R-squared=19%

0.710**

0.809**

Ability to participate in SRA

Satisfaction with SRA

Note: Errors in indicators not accounted for by a latent variable and errors in dependent variables not accounted for by predictors are omitted from this figure. Solid lines represented regression coefficients; dotted lines represent factor loadings. Standardized coefficients estimated by ML are presented. **p<0.001, †No p-value was reported because one factor loading for a latent variable was fixed at 1.0 and not estimated to scale the latent variable. Replicable and portable were dropped from the final model because these two indicators were not significantly loaded into the device quality.