the physical therapy clinical research network (ptclinresnet)

Authors:Carolee Winstein, PT, PhD, FAPTAPatricia Pate, MPT, NCSTingting Ge, MSCarolyn Ervin, PhDJames Baurley, MSKatherine J. Sullivan, PT, PhDSamantha J. Underwood, MSEileen G. Fowler, PT, PhDSara Mulroy, PT, PhDDavid A. Brown, PT, PhDKornelia Kulig, PT, PhDJames Gordon, PT, EdD, FAPTAStanley P. Azen, PhDPhysical Therapy Clinical Research

Network (PTClinResNet)*

Affiliations:From the Division of Biokinesiology andPhysical Therapy at the School ofDentistry, University of SouthernCalifornia, Los Angeles, California (CW,PP, KJS, KK, JG); Department ofPreventive Medicine, Keck School ofMedicine, University of SouthernCalifornia, Los Angeles, California (TG,CE, JB, SPA); Division of Biokinesiologyand Physical Therapy at the School ofDentistry, University of SouthernCalifornia, Los Angeles, California (SJU);Department of Surgery, Oregon Heath &Science University, Portland, Oregon(SJU); University of California, LosAngeles, Los Angeles, California (EGF);Rancho Los Amigos NationalRehabilitation Center, Downey, California(SM); and Department of PhysicalTherapy and Human MovementSciences, Feinberg School of Medicine,Northwestern University, Chicago, Illinois(DAB).

0894-9115/08/8711-0937/0American Journal of PhysicalMedicine & RehabilitationCopyright © 2007 by LippincottWilliams & Wilkins

The Physical Therapy ClinicalResearch Network (PTClinResNet)Methods, Efficacy, and Benefits of a RehabilitationResearch Network

ABSTRACT

Winstein C, Pate P, Ge T, Ervin C, Baurley J, Sullivan KJ, Underwood SJ, FowlerEG, Mulroy S, Brown DA, Kulig K, Gordon J, Azen SP; Physical Therapy ClinicalResearch Network (PTClinResNet): The Physical Therapy Clinical ResearchNetwork (PTClinResNet): methods, efficacy, and benefits of a rehabilitationresearch network. Am J Phys Med Rehabil 2008;87:937–950.

This article describes the vision, methods, and implementation strategies used inbuilding the infrastructure for PTClinResNet, a clinical research network de-signed to assess outcomes for health-related mobility associated with evidence-based physical therapy interventions across and within four different disabilitygroups. Specific aims were to (1) create the infrastructure necessary to developand sustain clinical trials research in rehabilitation, (2) generate evidence toevaluate the efficacy of resistance exercise–based physical interventions de-signed to improve muscle performance and movement skills, and (3) provideeducation and training opportunities for present and future clinician–researchersand for the rehabilitation community at-large in its support of evidence-basedpractice. We present the network’s infrastructure, development, and severalexamples that highlight the benefits of a clinical research network. We suggestthat the network structure is ideal for building research capacity and fosteringmultisite, multiinvestigator clinical research projects designed to generate evi-dence for the efficacy of rehabilitation interventions.

Key Words: Multisite Clinical Trials, Medical Rehabilitation, Outcomes Research,Disablement Model, Biomedical Informatics, Physical Therapy

November 2008 CT Clinical Research Network 937

REPORT & REVIEW

Outcomes

Disclosures:* Physical Therapy Clinical Research Network(PTClinResNet): The network’s principal investigator isCarolee J. Winstein, PhD, PT, FAPTA, and the coprincipalinvestigator is James Gordon, EdD, PT, FAPTA (both atUniversity of Southern California, Los Angeles, CA). Projectprincipal and coprincipal investigators include David A.Brown, PhD, PT (Northwestern University, Chicago, IL);Sara Mulroy, PhD, PT, and Bryan Kemp, PhD (Rancho LosAmigos National Rehabilitation Center, Downey, CA);Loretta M. Knutson, PhD, PT, PCS (Missouri StateUniversity, Springfield, MO); Eileen G. Fowler, PhD, PT(University of California–Los Angeles, Los Angeles, CA); andSharon K. DeMuth, DPT, Kornelia Kulig, PhD, PT, andKatherine J. Sullivan, PhD, PT (University SouthernCalifornia, Los Angeles, CA). The data-management center islocated at University of Southern California, Los Angeles,CA, and is directed by Stanley P. Azen, PhD. The four-member data safety and monitoring committee are NancyByl, PhD, PT, FAPTA, chair (University of California–SanFrancisco, San Francisco, CA), Hugh G. Watts, MD(Shriners’ Hospital for Children–LA Unit, Los Angeles, CA),June Isaacson Kailes, MSW (Western University of HealthSciences, Pomona, CA), and Anny Xiang, PhD (University ofSouthern California, Los Angeles, CA).

The Foundation for Physical Therapy supported thiswork. The authors have no proprietary or commercialinterest in any materials discussed in the manuscript.

Correspondence:All correspondence and requests for reprints should beaddressed to Carolee Winstein, PT, PhD, FAPTA, Division ofBiokinesiology and Physical Therapy, University of SouthernCalifornia, 1540 E. Alcazar St., CHP 155, Los Angeles, CA90089-9006.

In 2002, the National Institutes of Health (NIH)charted a roadmap for medical research for the21st century that included the development of re-search networks. The purpose of the roadmap wasto identify major opportunities and gaps in bio-medical research, and to include a vision andguidelines for implementation. The roadmap iden-tified the need to “build better integrated networksof academic centers linked to a qualified body ofcommunity-based health care providers.1”

Concurrent with the NIH roadmap initiative,and to facilitate cost-effective access to health care,academic institutions are seeking ways to formpartnerships that allow clinically relevant researchto be carried out in an efficient and effective man-ner. The structure of a clinical research networkwas identified as an ideal model because it allows adistributed organization, composed of researchteams and resources, to be combined in a strategicmanner to efficiently and effectively move forwardthe clinical translational research enterprise.

Other funding agencies have recognized thepotential for this research model. In particular, the

Foundation for Physical Therapy raised the fundsto develop a clinical research network that wouldadvance the science supporting the effectiveness ofphysical rehabilitation interventions. In addition,because the physical therapy profession is under-going rapid maturation as a doctoring profession,the expansion of research facilities and resourcescould serve the needs of the profession in achievingthe status required for autonomous, evidence-based practice.

The NIH roadmap drives rehabilitation re-search toward the next critical step to refinementof its clinical practice by reengineering the clinicalresearch enterprise. The next critical step for reha-bilitation medicine, and the one that is presentedin this paper, is to link together a group of leadinginterdisciplinary clinician–researchers and aca-demic institutions in a novel model of cooperationthrough the creation of an integrative, coherentclinical research network.

Emergent from this goal, the Physical TherapyClinical Research Network (PTClinResNet) was es-tablished in 2002. PTClinResNet consists of onecoordinating center, five primary satellite sites, andtwo sets of outpatient clinics (Fig. 1). The networkhas three specific aims: to (1) create the infrastruc-ture necessary to develop and sustain clinical trialsresearch in rehabilitation, (2) generate evidence toevaluate the efficacy of resistance exercise–basedphysical interventions designed to improve muscleperformance and movement skills, and (3) provideeducation and training opportunities for presentand future clinician–researchers in rehabilitationand for the rehabilitation community at-large in itssupport of evidence-based practice.

In its original proposal, PTClinResNet used theNagi disablement model2 as a framework for out-comes analysis. However, the network has sinceadopted the International Classification of Func-tions, Disability, and Health (ICF, Fig. 2), in largepart because of its acceptance by the broader com-munity of healthcare professionals and its greaterresearch potential to identify important interac-tions and links between outcome measures.3 TheICF was approved in May 2001 by the World HealthAssembly and is the successor to the InternationalClassification of Impairments Disabilities andHandicaps (ICIDH). Using rules that specify linksbetween items from specific instruments and cor-responding ICF categories,4 the representation ofICF components of body functions and structures,activities, participation, and contextual factors canbe investigated. The ICF is currently being appliedin clinical research and clinical practice in areas,including rehabilitation medicine.5 The ICF classi-fication is an effective organizational framework toallow assessment of outcome interactions that areassociated with health-related problems (i.e., body

938 Winstein et al. Am. J. Phys. Med. Rehabil. ● Vol. 87, No. 11

function/structure impairments, activity limita-tions, participation restrictions) and across dis-ease- or injury-specific health conditions.3

The purpose of this paper is to describe themethods and strategies used in accomplishingthe initial goals of building PTClinResNet, alongwith a summary of its current status and uniquebenefits. Specifically, the Methods section willdiscuss the infrastructure and development pro-cess, including the rationale for a network coor-dinating center. Included are details about thedifferent study designs, challenges for data ac-quisition, implementation, and data analyses.The Results section highlights examples andbenefits of a network collaboration. These in-clude example results of an exploratory meta-

analysis afforded by the network’s shared data-base and common disablement framework—specifically, across-project relationship(s) atbaseline for health status and measures of dis-ability severity. Finally, we describe examples ofspin-off projects and an NIH-funded phase IIIclinical trial that successfully leveraged the PT-ClinResNet infrastructure. The discussion sec-tion summarizes the benefits of a clinical re-search network, including its inclusion in anational registry (Inventory and Evaluation ofClinical Research Networks, IECRN), and makesrecommendations for how to avoid pitfalls andovercome barriers to the formation of clinicalresearch networks for future rehabilitation re-search.

FIGURE 1 The structure of PTClinResNet, with the umbrella coordination center providing oversight, datamanagement, education, training, and fiscal management for the four projects (left column) that willunfold across five satellite sites, and two sets of outpatient clinics (right column). One box representsseven outpatient pediatric clinics associated with the PEDALS project, and the bottom box represents17 different outpatient orthopedic clinics in the greater Los Angeles area, including USC PT associatesassociated with the MUSSEL project. Names of the clinics can be found in the Acknowledgmentssection. SAP, Scientific Advisory Panel; DMSB, Data Monitoring and Safety Board; STEPS, StrengthTraining Effectiveness Post Stroke; PEDALS, Pediatric Endurance Development And Limb Strength-ening; STOMPS, STrengthening and Optimal Movements for Painful Shoulders in chronic spinal cordinjury; MUSSEL, MUscle-Specific Strengthening Effectiveness post Lumbar microdiscectomy; USC,University of Southern California, Division of Biokinesiology and Physical Therapy; RLANRC, RanchoLos Amigos National Rehabilitation Center; NWU, Northwestern University, Department of PhysicalTherapy and Human Movement Sciences, Feinberg School of Medicine; MSU, Missouri State Univer-sity, Department of Physical Therapy. The outside brackets of the network sites column illustrate thepre-network institutional and/or collaborative links between sites.


CLINICAL RESEARCH METHODS INREHABILITATION ARE DEVELOPING

Clinical trials research in rehabilitation medi-cine is a relatively underdeveloped area of focus forclinician–scientists in rehabilitation fields.6 In re-sponse to a growing need for the development ofevidence-based approaches in rehabilitation, one ofthe first 2-day workshops on this topic was con-ducted in 1998 by the National Advisory Board onMedical Rehabilitation Research of the NIH–NICHD–NCMRR clinical practice programs. Theworkshop, Clinical Trials in Rehabilitation, was di-vided into four main sessions: (1) outcome mea-sures, (2) clinical trial design and selected disor-ders, (3) challenges in rehabilitation clinical trials,and (4) recommendations. To a large extent, theformation, mission, and design of PTClinResNetwere motivated by an understanding of the princi-ples, challenges, and barriers, and they are consis-tent with conclusions that emerged from thatworkshop. More recently, the American Journal ofPhysical Medicine and Rehabilitation has publishedan entire supplement devoted to the topic of clin-ical trials in medical rehabilitation that summa-rizes a later conference held on the same topic 5yrs ago in 2002.7

A COMMON THEMEOne of the initial aims of PTClinResNet was to

generate evidence to evaluate the efficacy of phys-ical therapy interventions. To accomplish this, weproposed four projects; one multisite phase II ran-domized clinical trial (RCT) and three phase I

RCTs. Each of the four projects focused on a dif-ferent disability group (adult stroke, pediatric ce-rebral palsy, chronic spinal cord injury, and low-back disorder) and identified and tested physicaltherapeutic strategies for improving function andparticipation. The common theme adopted by eachRCT was to design a therapeutic intervention studythat was based on the best available evidence andthat used a dynamic task-oriented or muscle-spe-cific approach to enhancing muscle performance.When completed, this comprehensive set of inves-tigations is expected to provide practical clinicalguidelines for rehabilitation practitioners that willinclude recommendations for optimal interventionstrategies for a variety of conditions, and to provideevidence that the intervention strategies are effec-tive in typical clinical settings.

THE ICF CLASSIFICATION PROVIDES AFRAMEWORK FOR PTCLINRESNET

Because of recent concerns for healthcare costcontainment and appropriate healthcare utiliza-tion, clinical outcomes in rehabilitation researchhave shifted toward investigations of interventionsthat improve outcomes at the level of individualfunction, participation, and quality of life (QOL).Clinical outcomes research in rehabilitation med-icine has progressed from earlier work, which fo-cused exclusively on the impairment (body func-tion/structure) level, to more recent work, whichfocuses on outcomes at the activity-limitation orparticipation-restriction level. With a growing bodyof evidence demonstrating the efficacy of physical

FIGURE 2 Visualization of the current understanding of interaction of various components of the InternationalClassification of Functioning, Disability and Health (ICF). The ICF does not model the “process” offunctioning and disability. It can be used, however, to describe the process by providing the meansto map the different constructs and domains. It provides a multiperspective approach to theclassification of functioning and disability as an interactive and evolutionary process. It provides thebuilding blocks for users who wish to create models and study different aspects of this process. In thissense, ICF can be seen as a language; the texts that can be created with it depend on the users, theircreativity, and their scientific orientation.3


therapy treatments,8–11 there is clearly a paradigmshift in rehabilitation research from descriptivestudies at each classification level to that whichprovides “direct evidence of the degree to whichphysical therapy that affects an impairment (e.g.,muscle force) will also reduce disability and im-prove the functional outcomes of the patient (i.e.,in activities such as transfers, walking ability, andimproved quality-of-life.”12 (p968)

Current trends in physical rehabilitation inter-vention effectiveness include the use of outcomemeasures that are not just limited to body struc-ture or function impairment (e.g., muscle func-tion) or activity restriction (e.g., reduced walkingvelocity) but that also include measures that reflecta person’s participation in meaningful life roles asmeasured by components of health status such asthe SF-36 and, more broadly, subjective QOL. Ev-idence of this paradigm shift is evident in therehabilitation research literature, with more stud-ies using outcome measures that relate to thevarious classification levels of the ICF model.13 Themedical community recognizes the importance ofmeasuring and addressing patient-reported out-comes (including QOL) and recognizes the need toimprove the measurement of these factors for dif-ferent disability groups.14,14a

With each RCT hosted within the network,there is at least one outcome specific to each of themajor levels of the ICF framework (body function/structure, activities, participation). Primary out-comes are those that are highly reliable, valid, andstudy specific (disability, population, specific aim).Further, primary outcomes are the ones that wereused for preproposal sample size estimates andthat, accordingly, are thought to be those out-comes most directly influenced by the interven-tion. Additionally, each study included a set ofsecondary outcomes using study-specific measuresat each major ICF level. Three studies used a com-mon overall measure of health status (i.e., SF-36),whereas the pediatrics RCT (PEDALS) used a sim-ilar but disability-specific health-related QOL mea-sure (i.e., PedsQL). Overall, and for each of the fourRCTs, we are testing the hypothesis that a specificphysical intervention that enhances muscle perfor-mance and functional movement skill can reducemovement-related body structure impairment andenhance functional performance and participation,and, in so doing, have a positive effect on healthstatus, participation, and/or QOL.15

METHODSPTClinResNet is organized through a center

that coordinates each project’s RCT developmentand implementation. The center’s infrastructureallows for centralized project design, project man-agement, and data analysis across the current four

clinical trials and multiple sites participating in thenetwork. In addition, the center provides a forumfor investigators with a common interest in reha-bilitation research to share their expertise. Thismultidisciplinary collaboration allows networknodes (i.e., clinical trials and sites) accessibility tospecialized resources, including biomedical infor-matics and biostatistics.

An important function of the coordinatingcenter is to develop standardized project protocolsand data-collection instruments that are consistentand easily accessible from any network node. ForPTClinResNet, a uniform standardized data dictio-nary was defined, allowing for multisite data entryand analysis and across-study comparisons. Build-ing from this design, it was possible to generatecomprehensive reports on project developmentprogress, participant safety, and dataset complete-ness. Effective communication is a key factor in anetwork with multiple studies and research sitesthat are located across large metropolitan areas aswell as across the country. The network coordinat-ing center facilitates communication between net-work investigators and clinicians and provides gen-eral information to the public sector. This wasaccomplished through the PTClinResNet Web site(http://pt.usc.edu/clinresnet). The Web site con-tains unrestricted information available to thephysical rehabilitation professional communityand potential study participants, and it also pro-vides an overview of the network, including rele-vant news items, conference information, and,more recently, our dissemination efforts. A docu-ment-management system is available for networkinvestigators and clinicians to share secure docu-ments. Monthly steering committee meetings al-low investigators and management personnel toaddress organizational issues and plan future net-work activities.

A clinical research network can provide essen-tial management oversight that can more effec-tively organize time constraints, determine net-work priorities, define individual project scope, andmonitor resource use. Ultimately, effective man-agement is needed to achieve research aims. Thecoordinating center provided project managementtools to assist PTClinResNet personnel in reachingthe network objectives. One important tool, thevirtual help desk, was developed to prioritize andassign network needs to the appropriate data-man-agement personnel. This mechanism improvesquality of service and provides a quantitative mea-sure of effort. Project scheduling is also important,to determine the critical path of activities neededto reach network goals. Quality-control reporting,along with primary and secondary data analyses, isimplemented chronologically according to time-lines reflected in the project schedule. Project


management tools are available to graphically rep-resent activities and deadlines.

In addition to project management, infrastruc-ture development for the network included theimplementation of a study data-acquisition and da-ta-management plan that included the followingfour elements: study design, data-acquisition de-sign, data-management implementation, and dataanalysis (Fig. 3). The following describes each ele-ment of this aspect of the infrastructure develop-ment.

Study DesignThe project investigators developed specific

measurement and intervention protocols that aredocumented in a common manual of procedures(MOP). The purpose of an MOP in clinical trials isto organize the design of the project, standardizemeasurement methods, and ensure consistent ap-plication of intervention protocols. The MOPserved to train and standardize the evaluators andinterventionists. Each site within the network canaccess the MOP(s) on the secure area of the PT-ClinResNet Web site. Access to each of the fourMOPs by all network investigators fostered system-atic and common practices across the sites foreverything from participant enrollment and ran-domization to data entry, and regulatory opera-tions such as maintaining IRB-approval trackingand reporting adverse events. Similarly, variousprotocols and standardization criteria were devel-oped by one study team that were adapted or mod-ified by another study team.

Data-Acquisition DesignBy using the MOP and collaborating with

project investigators, the coordinating center de-veloped the data-collection instruments. In PT-ClinResNet, four classifications of data-collectionforms were devised: participant information, eval-uation, intervention, and adverse events. In addi-tion to participant data, each form contains infor-mation on administration of the instrument,versioning, and relevant explanatory comments.Data instruments shared across studies were de-signed to be used network-wide (e.g., SF-36 wasused for all adult participants).

Once all data-collection forms were finalized,the data dictionary was developed. The data dictio-nary is a structure that stores metadata. The datadictionary serves a twofold purpose: creating thedatabase, and functioning as documentation fordata analysis. The data dictionary includes a de-scriptive variable name, the data type (integer,character, decimal), the specific question as itseems on the data-collection instrument, and thecoding and quality-control parameters for everyvariable. As an example, (a) variable name � gen-der, (b) data type � integer, (c) question on form �what is the participant’s gender? (d) codes � 1(male), 2 (female), (e) field length � 1 digit, and (f)quality control � required data point.

Data-Management ImplementationAfter a thorough analysis of the project and

data-acquisition designs, we implemented a data-management system consisting of the PTClinRes-

FIGURE 3 The PTClinResNet development process. The rows in the figure represent the phases of the develop-ment process (study design, data-acquisition design, implementation, and data analysis). The boxesare deliverables developed during each phase, and arrows show the relationships these deliverableshave on later phases in the development process.


Net database and Web-based data-entry applica-tions for each project. The database is designed tofollow a relational model, with each dataset repre-senting a data-collection instrument. Studies thatshared data-collection instruments had commondatasets. The database was normalized to avoid dataredundancy and to allow scalability in the size ofthe dataset. A dataset record represented the mea-surement for a specific participant at a particulartime point (e.g., baseline evaluation, postinterven-tion).

The Web-based applications allowed databasetransactions (i.e., the entry of data from the data-collection form to the structured query language(SQL) database, or the exporting of data from theSQL database to a SAS or SPSS database for anal-ysis and/or reporting purposes). This software wasdesigned to facilitate quick and accurate data entryfrom the hard-copy data-collection forms. The ap-plications were programmed to ensure data qualityand user-level security, and to ensure databaseintegrity. The data-entry application allowed thetherapist to create a new or find an existing par-ticipant. For new participant identifications, thesystem required clinical information and projectinclusion and exclusion data to be entered, to con-firm participant eligibility. The participant sum-mary page displayed important participant infor-mation and tracked their progress in the clinicaltrial (e.g., visits, evaluations). The applications in-cluded a global menu that remains constant forquick navigation between data-entry sections. Aquality-control feature used metadata from thedata dictionary to prevent common errors, such aspermissible range (i.e., minimum, maximum) inthe data dictionary to prevent or challenge poten-tial outliers.

Data AnalysisThe Web-based applications and statistical

software interfaced with the database through SQL.Various types of relationships were defined in SQLto join participant-enrollment data with informa-tion, evaluation, and intervention data. This al-lowed tracking and reporting of participantprogress through the project, customized datasetsfor statistical analysis, and enhanced cross-datasetintegrity checks. Data analysis was performed us-ing advanced statistical software tools that in-cluded SAS version 8.2, R Statistical Languageversion 2.1.0, and SPSS version 13.0.

In addition, the research network facilitatesinteractions between the investigative team andthe statistical team through steering committeemeetings, or through smaller subgroup discus-sions including PIs and data-management center(DMC) personnel. Productive interactions include(a) modifying analytic plans for specific hypotheses

because of unexpected covariates, noncomplianceof study participants, unexpected distributionalcharacteristics, etc., and (b) the identification andrecommendation of analytic plans for secondaryanalyses, including subgroup analyses, identifica-tion of prognostic factors within a given treatmentgroup, identification of treatment � mediatingvariable interactions, etc.

RESULTSIn this section, we briefly summarize the cur-

rent RCTs hosted by PTClinResNet, the benefit of aresearch network for comparisons across studies,and an overview of exemplar spin-off studies, in-cluding a phase III clinical trial that leveraged theinfrastructure described in the Methods section.

Current Clinical Projects Hosted onPTClinResNet

Figure 1 illustrates the four ongoing clinicalprojects that are hosted on PTClinResNet: (1)Strength Training Effectiveness Post-Stroke(STEPS) is the network’s flagship project, beingthe only phase II RCT currently hosted. The pri-mary objective is to determine whether functionalwalking outcomes in individuals with chronicstroke are improved with exercise programs thatinclude task-specific (i.e., body weight–supportedtreadmill training) or lower-extremity strengthtraining, or a combined exercise program that in-cludes both task-specific and strength training. (2)Pediatric Endurance and Limb Strengthening inspastic diplegic Cerebral Palsy Children (PEDALS)is a phase I investigation with a primary objectiveof determining the efficacy of a stationary cyclingintervention for treatment outcomes in childrenwith cerebral palsy. (3) Muscle Specific StrengthTraining Effectiveness after Lumbar Microdiscec-tomy (MUSSEL) is a phase I investigation to assessthe immediate and long-term effects of muscle-specific strengthening on decreasing disabilityand improving function and QOL in persons re-covering from lumbar microdiscectomy. (4)Strengthening and Optimal Movements for Pain-ful Shoulders in Chronic Spinal Cord Injury(STOMPS) is a phase I investigation with a pri-mary objective of determining the efficacy of acombined intervention comprising strengthen-ing exercises and instruction in optimal move-ments for painful shoulders in persons withchronic spinal cord injury.

Baseline Health Status and SeverityAcross RCTs

For each clinical trial, outcome measures offunction, disease- or injury-specific health status,and a common measure of health status (e.g., SF-


3616) were used. Table 1 uses baseline data andsummarizes the results of an exploratory omnibusanalysis to compare the effect sizes for varioushealth status scores (e.g., physical, mental) strati-fied by function, pain, or disability severity levelsfor each of the four projects. The effect size wascalculated as the difference in the mean healthstatus scores (more severe � less severe) divided bythe standard deviation of the health status scoresfor the less severe (impaired) group.

Because physical health was the primaryhealth-related variable that was impacted by sever-ity classification across all four studies, we con-trasted the impact of severity on physical healthusing the common metric of effect size (Fig. 4). Forthe SF-36,16 the physical health domain is a com-posite of four subscales: bodily pain, role physical,

general health, and physical functioning. Effectsizes ranged from 0.49 (STEPS) to 1.08 (MUSSEL),demonstrating the impact of the condition’s sever-ity on physical health. The large effect size in thepostsurgical acute low-back disorder group (i.e.,MUSSEL) as compared with the moderate effectsize in the chronic stroke groups (i.e., STEPS) isnot surprising, given that the physical health do-main of the SF-36 has a large component related tobodily pain. This comparison reflects the addedsensitivity that all components of the physicalhealth domain may have on a health conditionsuch as a low-back disorder, and the decreasedsensitivity in a health condition such as chronicstroke, where impaired mobility has a greater im-pact on physical health than pain. In contrast, theeffect size for the SIS-16,17 in the chronic stroke

TABLE 1 Significance and effect sizes of health status across function, pain, and disability severity levels

a. STEPS: SF-36 and SIS Health Status Scores Stratified by Severity of10-m Walk Test38

Health StatusScore

n1/n2

SeverityCategory 1,

SSV � 0.5 m/s

SeverityCategory 2,

SSV � 0.5m/s

ANOVAP

Value

EffectSize

SF-36 physical16 35/24 41.4 (7.4) 37.8 (8.5) 0.08 0.49SF-36 mental 25/24 54.5 (9.6) 52.0 (11.4) 0.36 0.26SIS-1617 44/32 61.9 (14.8) 49.9 (16.1) 0.001 0.80

b. PEDALS: PedsQL Health Status Scores Stratified by Severity of GMFCS20

PedsQL HealthStatus Score 21

n1/n2 Severity Category1, GMFCS Levels

I and II

Severity Category2, GMFCS Level

III

ANOVAP Value

EffectSize

Physical functioning 32/30 75.2 (19.3) 58.4 (21.1) �0.01 0.87Emotionalfunctioning

32/30 69.5 (22.9) 61.3 (22.5) 0.16 0.36

Social functioning 32/30 72.0 (21.1) 60.5 (21.1) 0.04 0.54School functioning 32/30 70.2 (18.2) 57.3 (19.2) 0.01 0.70Psychosocial healthsummary score

32/30 70.6 (17.3) 59.7 (16.8) 0.02 0.62

c. MUSSEL: SF-36 Health Status Scores Stratified by Severity of pain VASs39

Health Status Score n1/n2 Mild, 1 to <5 Moderate/Severe,5 to 10

ANOVAP Value

EffectSize

SF-36 physical 87/13 36.5 (8.2) 27.6 (4.3) �0.0001 1.08SF-36 mental 87/13 49.6 (10.8) 47.2 (9.9) 0.46 0.22

d. STOMPS: SF-36 Health Status Scores Stratified by Severity of WUSPI18

Health Status Score n1/n2 Severity Category1, 2.9 < 46.3

Severity Category2, 46.3–124.9

ANOVAP Value

EffectSize

SF-36 physical 38/37 39.0 (8.7) 31.5 (7.4) 0.0002 0.86SF-36 mental 38/36 57.6 (9.2) 46.7 (12.7) 0.0001 1.18

n1/n2, sample sizes for less severe vs. more severe categories; SSV, self-selected overground walkingvelocity; SIS, Stroke Impact Scale17; GMFCS, Gross Motor Function Classification System20; PedsQL, PediatricQuality of Life Inventory21; VAS, visual analog scales; WUSPI, Wheelchair Users Shoulder Pain Index.18


study was high (0.80), which illustrates the impor-tance of including disease-specific health statusinstruments as well as more general instruments ofhealth status (Table 1a).

For STOMPS, the effect size of initial pain levelon the physical health scale of the SF-36 was in-termediate between that of the STEPS and MUS-SEL studies. For the subjects in STOMPS, theinitial WUSPI18 scores were moderately correlatedwith both the bodily pain and the role physicalsubscales of physical health, but they were lessstrongly linked to the general health and physicalfunction subscales.19 This indicates that shoulderpain is impacting the participants’ ability to fulfillthe physical demands of their life roles. The phys-ical function subscale is less affected by shoulderpain, however, because several of those activitiesare related to walking and stair climbing, which arelimited by the spinal injury itself, but not theshoulder pain. In contrast, for individuals with alow-back disorder (i.e., MUSSEL), the relationshipbetween pain levels and walking function would beexpected to be more strongly linked.

The severity categories used for PEDALS con-trasts children who are able to walk independentlywithout assistive devices (GMFCS levels I and II)20

vs. those who require walkers or canes (GMFCSlevel III).20 The physical functioning scale score ofthe PedsQL21 is focused on a child’s ability to walk,

run, play, lift heavy objects, and perform chores. Allof these functions are greatly affected by functionalmobility and explain the high effect sizes shown inTable 1b and Figure 4. Two other scale scores,social functioning and school functioning, includeitems that are related to functional mobility andseverity of disability, such as the ability to keep upwith others at play and missing school for medicalappointments. These scales also demonstrate sig-nificance between severity groups based on GMFCSlevel with relatively large effect sizes. In contrast,the emotional functioning scale score did not dem-onstrate significance between severity groups or alarge effect size. This subscale focused on thechild’s feelings and did not include any questionsrelated to physical function.

Spin-off Projects and a Phase III RCTUsing the Network Infrastructure

One important benefit of a clinical researchnetwork is the opportunity to leverage the infra-structure for future clinical trials. Investigatorsassociated with PTClinResNet (K. Sullivan, S.Azen) were recently awarded an NIH clinical trialgrant from NINDS to conduct a phase III definitiveRCT, Locomotor Experience Applied Post-Stroke(LEAPS; NIH/NINDS/NCMRR NS05056, P. W. Dun-can, PI). The primary goals of the LEAPS trial areto determine whether a specialized locomotor

FIGURE 4 Impact of disability on HRQL physical health score, using effect sizes for the four projects hosted byPTClinResNet. A comparison of the effect sizes for physical health status scores across the fourprojects: STEPS, PEDALS, MUSSEL, and STOMPS. The effect size was calculated as the difference in themean physical component scores (more severe � less severe) divided by the standard deviation of thephysical component for the less severe group. HRQL, Health-Related Quality of Life; STEPS, StrengthTraining Effectiveness Post Stroke; PEDALS, Pediatric Endurance Development and Limb Strengtheningfor children with cerebral palsy; MUSSEL, Muscle-Specific Strengthening Effectiveness post Lumbarmicrodiscectomy; STOMPS, Strengthening and Optimal Movements for Painful Shoulders. STEPS, MUS-SEL, and STOMPS effect size measurements are based on the physical health score of the SF-36, and thePEDALS effect size measurement is based on the physical functioning subscale of the PedsQL).


training program that includes use of a bodyweight–support system and a treadmill as a treat-ment modality will result in a functionally signifi-cant improvement in 1-yr walking outcomes ofindividuals after stroke compared with a nonspe-cific, low-intensity exercise comparison group. TheLEAPS trial will also address whether the timing oftherapy, severity of impairments, and number oftreatments affect outcomes. Components devel-oped for PTClinResNet, including the standardizedmeasurement and intervention protocols and re-sources from the infrastructure, were integratedinto the LEAPS MOP to build the multisite data-base for data entry, standard report generation(e.g., actual vs. projected recruitment, quality-con-trol reports, posting of steering committee min-utes), and a discussion board to facilitate investi-gator communication.

Several other spin-off studies also used tools thatwere initially developed for PTClinResNet. For exam-ple, a study recently funded by NIDDR (D. Brown,co-lead investigator, STEPS) used the body weight–support treadmill training protocol and the standard-ized measurement procedures developed for STEPSto investigate the use of a virtual reality system forimproving walking outcomes during treadmill train-ing in people poststroke (NIDRR H133G050132).Sara Mulroy (co-lead investigator, STOMPS) was re-cently awarded a 5-yr NIH grant from the NationalCenter for Medical Rehabilitation Research (NCMRRHD049774) to study the risk factors for shoulder painin patients with spinal cord injury.

At the coordinating institution, federated da-tabases are being developed from elements of theDMC for PTClinResNet to integrate clinical, mag-netic resonance imaging, and functional magneticresonance imaging data, with the goal of promot-ing interesting data queries to better understandthe mechanisms associated with improvement intreatment outcomes. This work has been fueled byan NIH Roadmap initiative to promote exploratorycenters for interdisciplinary research. Our projectaims to establish a center for new directions instroke rehabilitation (RR-04-002, T. McNeill, PI; C.Winstein, co-PI).

DISCUSSIONClinical Trials Research Will Benefit fromClinical Trial Network Collaboration

The team of scientists collaborating under theumbrella PTClinResNet is a diverse group of re-searchers whose primary research training providesexpertise across relevant but diverse disciplines in-cluding biomechanics, kinesiology/movement sci-ence, clinical psychology, health services, and neuro-rehabilitation. This team of clinical scientists hasestablished track records that can be leveraged for the

design and testing of optimal rehabilitation interven-tions. In other words, the collaborative team is wellsuited to translate the relevant science into clinicalpractice in rehabilitation medicine. In addition, theuniform structure permits leveraging the leadershipand strategies of the phase II flagship RCT to theother three phase I RCTs.

We suggest that foundational research in thesocial, cognitive, and neurosciences can have auseful, long-lasting impact on the development oftheoretically defensible rehabilitation practices.22

Historically, physical rehabilitation practiceevolved in part from practical needs (e.g., WWIIinjuries) and was influenced primarily by a physi-ologic/medical perspective. The need to fostertranslation of the biological and physical sciencesinto clinical practice has been recognized for sometime in medical education.23,24 Recently, we haveseen in several cases that preclinical work usinganimal models of injury have begun to influencethe development of new practices of rehabilitation.This is most evident from the introduction of suchinterventions as constraint-induced movementtherapy for upper-extremity recovery in stroke25,26

and body weight–assisted gait training in spinalcord injury27 and stroke.28 However, the progres-sion from discovery to preclinical to phase III RCTcan be protracted.29 For example, the recently pub-lished phase III EXCITE trial of constraint-inducedmovement therapy is the first multisite random-ized trial to demonstrate the efficacy of a rehabil-itative intervention. In so doing, it moves neuro-rehabilitative care into the area of evidence-basedmedicine.30,31 However, the EXCITE results werepublished 25 yrs after the first case study to applyconstraint-induced movement therapy, and 17 yrsafter the first phase I clinical trial study32 designedto test the feasibility and efficacy of this approachin hemiparetic adults. We suggest that one impor-tant benefit of a clinical trials network with itscollective expertise of the investigative team andthe centralized data-management infrastructure isto provide the means for more efficient and effec-tive progression through the translational steps ofclinical research to evidence-based practice.

Other benefits of the PTClinResNet networkcollaboration have included (1) the opportunity toinvestigate interactions within and between dis-ability groups. The shared ICF framework withsome shared outcome measures such as the healthstatus inventory, SF-36, allow for exploratory anal-yses across disability groups; (2) an opportunity toprovide education and training to a wide array ofstudents, professionals, and junior investigators;and (3) an opportunity to impact clinical practicethrough clinic-based research that requires stan-dardization of assessments and intervention proto-cols. To this end, several of the network investiga-


tors have begun to disseminate descriptions of theclinical protocols through the open-access BMCjournal.33 Furthermore, the network served as aresource for a multisite study on the diagnosisreferral process to physical therapy.34

Contributions of the PTClinResNetScientific Team

The network investigative team comprises thenetwork PI and co-PI, project lead investigator,co-leads, director of the data-management center,network project manager, clinical research coordi-nators, blinded/masked evaluators, interventiontherapists, and an extensive group of data-entrypersonnel. Benefits of a comprehensive investiga-tive team such as this include not only the oppor-tunities for multisite and multidiscipline collabo-ration, but the practical aspects of increasedparticipant-recruitment potential, thereby facilitat-ing the attainment of larger sample sizes, whichare necessary for more robust tests of effectiveness.The network is one way to overcome the singlebiggest problem in clinical trial research: failure torecruit to target goals.35 In addition, the multidi-agnostic database lends itself to various forms ofmeta-analysis across diagnoses, geographical re-gions, ages, and demographics (see, for example,Fig. 4). By responding to the NIH Roadmap rec-ommendation for clinical research networks (andteams), PTClinResNet and the investigative teamplaced itself at the forefront of clinical research de-signs in rehabilitation, which, in turn, was success-fully leveraged for a phase III definitive RCT (LEAPS).

PTClinResNet Is a Member of a NationalRegistry

PTClinResNet is a recognized member of theInventory and Evaluation of Clinical Research Net-works (IECRN).36 The IECRN, a component of theNIH Roadmap, is an inventory of health-relatedclinical research networks that have establishedrequirements. For example, a clinical research net-work must conduct human subjects research thatis relevant to improving the quality of humanhealth, have scientific leadership that evaluates re-search ideas, and have a minimum of three inde-pendent entities. To date, there are 241 registerednetworks at IECRN. Of these, PTClinResNet is one ofonly a handful of these networks designed to addresspostacute repair, recovery, and rehabilitation.

Recommendations for Development ofFuture Networks

In retrospect, we appreciate that many lessonswere learned during the development and imple-mentation process. From the study-design phase,we recommend implementation of a policy that

requires all data-collection forms to be designed forthe user (evaluator/intervention therapist), to en-sure that the paper-based form and Web-based da-ta-entry screen are as consistent as possible. Thispolicy will eliminate “guesswork” for the data-entrypersonnel and will prevent at least one source oferror in the data-entry process. When forms areupdated during the trial, we recommend immedi-ate Web posting and automated e-mail notificationto all appropriate personnel of the updates and anyform changes, to ensure that the latest and mostupdated version is used.

At the data-implementation phase, we recom-mend immediate data entry and data checking afterdata collection, with no more than a 10-day delaybetween acquisition and entry. Many of the data-entry and analysis problems we encountered wereattributable to an unintentional delay between col-lection/acquisition and data checking. For exam-ple, data-entry persons would find missing valuesin the data-collection form and have to try to trackdown the evaluator, who may have left the projector did not remember what should have been en-tered. We recommend a budget item for hiring andtraining designated data-entry personnel whose jobis dedicated to timely data entry. Similarly, duringheavy subject-recruitment periods, hiring andtraining extra personnel to assist in screening andrecruitment will be well worth the added expense.Finally, during heavy periods of data analysis, hir-ing and training extra personnel who can assist theDMC in number crunching, writing code, andcross-checking would be another worthwhile ex-pense that can have high pay-offs related to timelydissemination of results.

Other recommendations that may be simplis-tic or obvious but are essential include the oppor-tunities for regular communication between inves-tigators, evaluators, interventionists, and projectstaff. Using the example of PTClinResNet, commu-nication strategies included monthly conferencecalls with the steering committee, weekly emailupdates about recruitment and other business asappropriate, annual reports, and annual investiga-tor meetings to the Physical Therapy Foundation.All decisions and discussions were documented andarchived for later reference as steering committeeminutes, summary documents, and all emails.Lastly, for overall success, it is important to fosteran attitude of flexibility and support within thescientific and clinical teams—especially when un-anticipated opportunities arise that require jug-gling priorities (e.g., conference presentations, ab-stract deadlines). Respect for the cross-disciplinedifferences that are part of the strength of anyinterdisciplinary collaboration is important—inthe end, these diverse perspectives are what makethe network team stronger. One should recruit


expert clinical scientists into the team who under-stand the nature of interdisciplinary collaborationand who will be invested in the goals and oppor-tunities that a network affords. Finally, when con-structing a budget for a network, considerationshould be given to many of the hidden costs asso-ciated with running the network. In the case ofPTClinResNet, several investigators sought out ad-ditional supplemental funds that included partici-pating institutional support in the form of donatedindirect facilities and administration costs, gradu-ate student training grant funds, various fundrais-ing efforts from private sources (donations), andvolunteering their own personal time.

The future of PTClinResNet is still under con-sideration. One plan is to make the network avail-able to other investigators who are interested inexpanding the scope of their clinically related re-search. In this way, the network will grow in num-bers of investigators but still maintain a strongcore of services related to coordinating centerfunctions. Another plan is to align the networkwith efforts to develop a clinical and translationalsciences institute. This latter strategy would capi-talize on the NIH Roadmap initiatives to fosterinterdisciplinary clinical research teams in rehabil-itation. To a large extent, the ultimate fate of thisnetwork will be determined by the vision and valueplaced on interdisciplinary rehabilitation researchcollaborations both inside and out of academicmedical centers. There has been considerable de-bate of late within the rehabilitation communityabout these issues and the need to develop researchcapacity in rehabilitation medicine.37 We are hope-ful that in the future there will be a broader visionfor the value of interdisciplinary collaborativeteams, especially for the kinds of complex problemsthat emerge in conditions of chronic disability.

ACKNOWLEDGMENTSThis project was supported by a grant from the

Foundation for Physical Therapy to establish PT-ClinResNet: a clinical research network to evaluatethe efficacy of physical therapist practice. Addi-tional funding from NIDRR to Bryan Kemp, PhD,supported the STOMPS project. The authors ac-knowledge Jennifer Bandich, USC research admin-istrator; Victoria Matthews, from the Foundationfor Physical Therapy; Judith M. Burnfield, for de-sign of the network figure; and the following in-vestigators, project coordinators, interventiontherapists, blinded evaluators, data-managementpersonnel, surgeons, translators, clinics, subjects,and their families (the latter not listed):

STEPSLead Investigators: David A. Brown, PT, PhD,

Katherine Sullivan, PT, PhD, Sara Mulroy, PT, PhD

Project Coordinator: Tara Klassen Ms, PT, NCSIntervention Therapists: Carolina Carmona,

PT, Bernadette Currier, DPT, NCS, Nicole Furno,PTA, Robbin Howard, DPT, NCS, Nicole Korda, PT,Didi Matthews, DPT, NCS

Intervention Therapy Assistants: Maria Caro,Louis Iberra

Blinded-to-Group-Designation Evaluators:Sandy Conners, PTA, Valerie Eberly, PT, NCS,JoAnne Gronley, DPT, Allie Hyngstrom, MSPT,Betsy King, DPT, Barbara Lopetinsky, PT, CraigNewsam, DPT, Lynn Rogers, MS, Sheila Schindler-Ivens, PT, PhD, Jennifer Whitney, PT, Arlene Yang,MSPT, NCS

Data Personnel: Joanne Delosreyes, DPT, ChrisHahn

Clinics: Department of Physical Therapy andHuman Movement Sciences, Feinberg School ofMedicine, Northwestern University, Rancho LosAmigos National Rehabilitation Center, USC PTAssociates

PEDALSLead Investigators: Eileen G. Fowler, PT, PhD,

Loretta M. Knutson, PT, PhD, PCS, Sharon K.Demuth, DPT

Project Coordinators and Blinded Evaluators:Kara Siebert, MSEd, DPT, Mia Sugi, DPT, VictoriaSimms, MPT

Intervention Therapists: Vasti Blake, PTA, Tan-jay Castro, DPT, Kara Crockett, PT, Nancy Egizii,MPT, Noel Marie Enriquez, PT, PCS, David Euro-pongpan, DPT, Amanda Glendenning, PT, DianneE. Jones, PT, MEd, Betsy King, DPT, Jean Knapp,PT, PCS, Rennie T. Lee, PT, Barbara Lopetinsky,PT, Nisha Pagan, MPT, PCS, Tracy Phenix, PT,Deborah Rothman, MSPT, Susan Rouleau, PT,Christy Skura, DPT, Josie Stickles, DPT, Margare-tha Van Gool, PT, PCS, Julie Yang, DPT

Outcome Data-Collection Personnel and Facil-ities: George Salem, PhD, Matt Sandusky, RichSouza, MPT, Albert Vallejo, PhD, Francisco BravoMedical Magnet High School

Data Personnel: Frances Chien, Kyle Fink,Evan Goldberg, MS, Michelle Hudson, MPT, KarinaKunder, Serge Modoyan, Sarah Mohajeri, TerencePadden, BS, Wenli Wang, MS

Interpreters: Nena Becerra, Lidia Cortes, Car-men Diaz, Wil Diaz, DPT, Karla Cordova, DPT,Minchul Jung, Linda Kang, Kevin Lee, DPT, RaulLona, DPT, Irene Morado, Susumu Ota, PT, JanelleRodriquez, Pietro Scaglioni-Solano, MS, JooeunSong, MS

Recruitment: California Children’s Services(Lisa Mena, MPT, Eric Lingren, MPT, and thePerez, South Bay, Miller, and Orange County Med-ical Treatment Units)


Corporate Donations or Discounts: Helen’s Cy-cles, Santa Monica, Freedom Concepts, NationalAMBUCS, Inc., Sam’s Club

Volunteers and Foundations: Caitlin Fowler,Ernie Meadows, Sidney Stern Memorial Trust,Steinmetz Foundation, Sykes Family Foundation,UCP Research and Education Foundation

Clinics: Meyer Center at CoxHealth, Orthopae-dic Hospital, Pediatric Therapy Network, PhelpsCounty Community Center, St. John’s Lebanon,The Children’s Therapy Center, Therapy West

MUSSELLead Investigator: Kornelia Kulig, PT, PhD,

Christopher Powers, PT, PhDProject Coordinators: Allison Bursch, DPT, Eli

Denham, BS, Kimiko Yamada, DPT, ATCIntervention Therapists: Brandon Arakaki,

MPT, Julie Barker, PT, Zirna Basilio, DPT, JoeBaumgaertner, MPT, Kirk Bentzen, PT, DPT, JonBergh, MPT, Julia Burlette, DPT, OCS, Allison Bur-sch, DPT, Heather De Cordova, MPT, OCS, BryanDennison, MPT, Link Elliot, DPT, Rosalia Felahy,MPT, Sarah Grannis, DPT, ATC, Julie Guthrie,DPT, Larry Ho, DPT, OCS, Sally Ho, DPT, EricJarvina, MPT, CSCS, Geoff Kandes, DPT, CSCS,Dwight Kelsey, PT, MA, Mark Kirsch, MPT,Danielle Orland, DPT, Elizabeth Poppert, DPT,OCS, Gretchen Roehr, DPT, Richard Rossman,MPT, Aldona Shahin, PT, Lisa Shepard, DPT, SusieSouza, MPT, Robert Spang, DPT, CSCS, Ako Wa-kano, PT, Cathryn Williams, PT, Halle Wilson, DPT

Blinded-to-Group-Designation Evaluators:George Beneck, MS, PT, OCS, J. Raul Lona, DPT,ATC, John M. Popovich, Jr, DPT, ATC, David M.Selkowitz, PhD, PT

Data Personnel: Danny Asami, MS, PhillipDavis, BS, Karen Iwanaga, BS, Courtney Nevelow,BS, Jason Villareal, DPT

Surgeons: Greg Carlson, MD, William Costi-gan, MD, Thomas Chen, MD, PhD, Mitchell Cohen,MD, Jeff Deckey, MD, William Dillin, MD, RichardFeldman, MD, Larry Teik-Man Khoo, MD, PabloLawner, MD, Scott Lederhaus, MD, Mark Liker,MD, Charles Liu, MD, Jim Loddengaard, MD, Wil-liam Mouradian, MD, Sylvain Palmer, MD, TiffanyRogers, MD, MPT, Srinath Samudrala, MD, A. NickShamie, MD, Mark Spoonamore, MD, Jeffrey C.Wang, MD, Michael Wang, MD, Michael Weinstein,MD

Clinics: Atlantis Physical Therapy, Gary Souza,PT, and Associates, Glendale Adventist MedicalCenter, Harbor Physical Therapy & Sports Medi-cine Clinic, Henry Mayo Newhall Memorial Hospi-tal, Heritage Physical Therapy, Ho Physical Ther-apy, Ho Rehab, Huntington RehabilitationMedicine, Inland Empire Physical Therapy, Kern &Associates Physical Therapy, Layfield & Associates

Physical Therapy, Mariners Physical Therapy, Or-thopedic Specialty Institute, Pomona Valley Hospi-tal Medical Center, ProSport Physical Therapy,USC PT Associates

STOMPSLead Investigators: Bryan Kemp, PhD, Sara

Mulroy, PT, PhDProject Coordinator: Dee Gutierrez, PTIntervention Therapists: Craig Newsam, DPT,

Jennifer Whitney, PTBlinded-to-Group-Designation Evaluators:

Valerie Eberly, PT, NCS, Lisa Haubert, MPTData Personnel: Cynthia Kushi, OT, Claire

McLean, Jitka Kliers, PT, Charles WhiteheadClinic: Rancho Los Amigos National Rehabili-

tation Center

REFERENCES1. NIH Roadmap for Medical Research Web site. Available at:

http://nihroadmap.nih.gov. Accessed July 25, 2006

2. Nagi SZ: Disability concepts revisited: implications for pre-vention, in Pope AM, Tarlov AR (eds): Disability in America:Toward a National Agenda for Prevention. Washington, DC,National Academy Press, 1991, pp 309–27

3. World Health Organization. International Classification ofFunctioning, Disability and Health. Geneva, World HealthOrganization, 2001

4. Cieza A, Brockow T, Ewert T, : Linking health-status mea-surements to the international classification of functioning,disability and health. J Rehabil Med 2002;34:205–10

5. Stucki G, Ewert T, Cieza A: Value and application of the ICFin rehabilitation medicine. Disabil Rehabil 2002;24:932–8

6. Tate DG, Findley T Jr, Dijkers M, Nobunaga AI, Karunas RB:Randomized clinical trials in medical rehabilitation research.Am J Phys Med Rehabil 1999;78:486–99

7. American Journal of Physical Medicine & Rehabilitation.Clinical Trials: The Cornerstone of Medical Rehabilitation.October 2003;82(10 suppl):S1–60

8. Kwakkel G, Kollen BJ, Wagenaar RC: Long term effects ofintensity of upper and lower limb training after stroke: arandomised trial. J Neurol Neurosurg Psychiatry 2002;72:473–9

9. Kwakkel G, Wagenaar RC, Twisk JW, Lankhorst GJ, KoetsierJC: Intensity of leg and arm training after primary middle-cerebral-artery stroke: a randomised trial. Lancet 1999;354:191–6

10. Van Peppen RP, Kwakkel G, Wood-Dauphinee S, HendriksHJ, Van der Wees PJ, Dekker J: The impact of physicaltherapy on functional outcomes after stroke: what’s theevidence? Clin Rehabil 2004;18:833–62

11. Canadian Stroke Network: Available at: http://www.canadianstrokenetwork.ca/. Accessed February 5, 2007

12. Jette AM: Outcomes research: shifting the dominant re-search paradigm in physical therapy. Phys Ther 1995;75:965–70

13. Cieza A, Stucki G: Content comparison of health-relatedquality of life (HRQOL) instruments based on the interna-tional classification of functioning, disability and health(ICF). Qual Life Res 2005;14:1225–37

14. Tulsky DS, Rosenthal M: Quality of life measurement inrehabilitation medicine: building an agenda for the future.Arch Phys Med Rehabil 2002;83(Suppl 2):S1–3

14a.Tulsky DS, Rosenthal M: Measurement of quality of life inrehabilitation medicine: emerging issues. Arch Phys MedRehabil 2003;84(Suppl 2):S1–2


15. Wilson IB, Cleary PD: Linking clinical variables with health-related quality of life. A conceptual model of patient out-comes. JAMA 1995;273:59–65

16. Ware JE Jr, Sherbourne CD: The MOS 36-item short-formhealth survey (SF-36). I. Conceptual framework and itemselection. Med Care 1992;30:473–83

17. Duncan PW, Lai SM, Bode RK, Perera S, DeRosa J: StrokeImpact Scale-16: a brief assessment of physical function.Neurology 2003;60:291–6

18. Curtis KA, Roach KE, Applegate EB, : Development of theWheelchair User’s Shoulder Pain Index (WUSPI). Paraplegia1995;33:290–3

19. Mulroy S, Gutierrez D, Klier J, Kemp B: Strengthening andoptimal movements for painful shoulders in chronic spinalcord injury. Paper presented at: APTA Combined SectionsMeeting, San Diego, 2006

20. Wood E, Rosenbaum P: The gross motor function classifi-cation system for cerebral palsy: a study of reliability andstability over time. Dev Med Child Neurol 2000;42:292–6

21. Varni JW, Seid M, Kurtin PS: PedsQL 4.0: reliability andvalidity of the Pediatric Quality of Life Inventory version 4.0generic core scales in healthy and patient populations. MedCare 2001;39:800–12

22. Ochsner KN, Lieberman MD: The emergence of social cog-nitive neuroscience. Am Psychol 2001;56:717–34

23. Gray ML, Bonventre JV: Training PhD researchers to trans-late science to clinical medicine: closing the gap from theother side. Nat Med 2002;8:433–6

24. Smith JJ, Koethe SM, Forster HV: Bridging the medicalschool gap—pathophysiology links basic science, clinicalmedicine. Scientist 1998;12:9

25. Taub E, Wolf SL: Constraint induced movement techniquesto facilitate upper extremity use in stroke patients. TopStroke Rehabil 1997;3:38–61

26. van der Lee JH: Constraint-induced movement therapy:some thoughts about theories and evidence. J Rehabil Med.2003;41(suppl):41–5

27. Harkema SJ, Hurley SL, Patel UK, Requejo PS, Dobkin BH,

Edgerton VR: Human lumbosacral spinal cord interpretsloading during stepping. J Neurophysiol 1997;77:797–811

28. Barbeau H, McCrea DA, O’Donovan MJ, Rossignol S, GrillWM, Lemay MA: Tapping into spinal circuits to restoremotor function. Brain Res 1999;30:27–51

29. Barbeau H, Fung J: The role of rehabilitation in the recov-ery of walking in the neurological population. Curr OpinNeurol 2001;14:735–40

30. Wolf SL, Winstein CJ, Miller JP, : Effect of constraint-induced movement therapy on upper extremity function 3to 9 months after stroke: the EXCITE randomized clinicaltrial. JAMA 2006;296:2095–104

31. Luft AR, Hanley DF: Stroke recovery—moving in an EX-CITE-ing direction. JAMA 2006;296:2141–3

32. Wolf SL, Lecraw DE, Barton LA, Jann BB: Forced use ofhemiplegic upper extremities to reverse the effect of learnednonuse among chronic stroke and head-injured patients.Exp Neurol 1989;104:125–32

33. Selkowitz DM, Kulig K, Poppert EM, : The immediate andlong-term effects of exercise and patient education on physical,functional, and quality-of-life outcome measures after single-level lumbar microdiscectomy: a randomized controlled trialprotocol. BMC Musculoskelet Disord 2006;7:70

34. Davenport TE, Watts HG, Kulig K, Resnik C: Current statusand correlates of physicians’ referral diagnoses for physicaltherapy. J Orthop Sports Phys Ther 2005;35:572–9

35. Marks RG, Conlon M, Ruberg SJ: Paradigm shifts in clinicaltrials enabled by information technology. Stat Med 2001;20:2683–96

36. IECRN: Inventory and Evaluation of Clinical Research Net-works. Available at: https://www.clinicalresearchnetwork-s.org/IECRNProject.asp. Accessed February 5, 2007

37. Frontera WR, Fuhrer MJ, Jette AM, : Rehabilitation medi-cine summit: building research capacity. Executive sum-mary. J Neuroeng Rehabil 2006;3:1

38. Perry J, Garrett M, Gronley JK, Mulroy SJ: Classification ofwalking handicap in the stroke population. Stroke 1995;26:982–9

39. Scott J, Huskisson EC: Graphic representation of pain. Pain1976;2:175–84


the physical therapy clinical research network (ptclinresnet)

Documents