Overview of Progress Carolee Winstein, PhD, PT, FAPTA, Principal Investigator

1. PTClinResNet Overview of Progress Carolee Winstein, PhD, PT, FAPTA, Principal Investigator Annual Investigators Meeting: Boston, MA, June 8, 2005

2. Overview of Progress • Since last meeting, June 29, 2004 • Progress relative to specific aims for PTClinResNet • Recruitment • Quality assurance • Data management • Dissemination and training

3. PTClinResNet has three aims: • To conduct four Randomized Clinical Trials (RCT) to evaluate efficacy of practice • STEPS, MUSSEL, PEDALS, STOMPS • Build the infrastructure to support clinical trials research • Education and training (knowledge transfer) These three are not mutually exclusive but, instead, mutually inter-dependent

4. Aim1: Four Randomized Controlled Trials: • STEPS: Strength-training effectiveness post stroke • David Brown, Ph.D., P.T., , Kathy Sullivan, PhD, PT, Reporting, Recruitment N = 80 (41)/80* • MUSSEL: Muscle specific strengthening effectiveness post lumbar microdiscectomy • Kornelia Kulig, Ph.D., P.T., Reporting, Recruitment N = 73 (23)/100 • PEDALS: Pediatric endurance development and limb strengthening • Eileen Fowler, Ph.D., P.T., Reporting, RecruitmentN = 53 (26)/60 • STOMPS: Strengthening and optimal movements for painful shouldersin chronic spinal cord injury • Sara Mulroy, Ph.D., P.T., and Dee Gutierrez, PT, Reporting, RecruitmentN = 64 (21)/80 *Current-June 6, 2005 (AIM 2004)/ Total

5. ….“and now the numbers” (NPR, Market Place) • Proposed N across the four RCTs = 320 • Current N across the four RCTs = 270 (111) • 270 (111)/320 = 84% (35%) of final target • Relative to other large-scale multi-site RCTs, this represents an excellent and sustained progress on the recruitment front. • In the last year, there has been a 2.4 fold increase in recruitment over the first 1.5 yrs of the life of the network.

6. Comparison of EXCITE & PTClinResNet 2004 2005

7. Aim 1: ‘Conduct’ four RCTs • Each project team has: • Refined, revised, and evaluated the study design, intervention protocols, assessments, recruitment procedures, timelines, data forms, DMSC reports, data completion, data input and planned analyses. • Developedand maintained all standardization procedures for interventions and assessments. • Trained all masked raters and interventionists using a 90% criterion level prior to data collection and standardized when new staff are hired • Trained all study personnel for database input. • Conformed and been approved and renewed by local IRB; HIPAA compliance guidelines (regulatory training).

8. Aim 2: Build the infrastructure to support clinical trials research. • Project manager: Provides the ‘glue’ to facilitate essential communicationbetween: • Coordinating site and Project teams • Monthly Steering Committee Conference calls and posting minutes • Weekly updates of DMC changes, recruitment graphs • Enrollment and randomization • Project teams and DMC • Development of forms, data dictionaries, testing of web entry system, training of data entry personnel; DMSC reports; data requests • DMC and Coordinating site (weekly to bi-monthly meetings) • Coordination with DMSC; development of current reports; primary analysis algorithms and SAS code

9. Aim 2: Part of this infrastructure is an experienced project manager. • Project manager: Samantha Underwood/Tricia Pate provides the ‘glue’ to facilitate essential documentation including: • Tracking and Follow-up • Status of standardization for each project personnel • Status of Adverse Events and reporting to DMSC • Posting on website; monitoring and training data entry • Progress reports to Foundation • Status of subcontracts and coordinating site budget (with Co-PI, Gordon) • Compiling all information for progress reports • Documentation and record keeping • All data are backed up; all forms are de-identified • Patient ID records are kept in locked cabinets

10. Aim 2: Build the infrastructure to support clinical trials research. • Centralized Data Management • Stanley Azen, Ph.D. (+ DMC Team) • Director of Statistical Consulting and Research Center (SCRC) • DMC Team • James Baurley,MS, TingTing Gee, MS, Carolyn Ervin, PhD, • Manual of Procedures, including all standardization procedures • Data Dictionary (defines all data codes, time frames etc.) • Paper forms to Web Data Entry (data entry manual--compatibility) • Embedded SAS code for current and later analysis (current effort) • Recruitment and randomization-study specific (SU/TP with DMC) • Coordinate and provide reports to external oversight committee (DMSC) • Consult on modifications to design, randomization blocking etc when needed.

11. Data Dictionary

12. % Data entered relative to current n by study • PEDALS: 51% • STEPS: 100% • STOMPS: 16% • MUSSEL: 93% • Number of errors corrected—from checked data only • STEPS: 1% (103/10,207 checked fields)

13. Aim 3: Provide education and training opportunities for present and future clinician-researchers in physical therapy and for the physical therapy practice community at-large in its support of evidence-based practice.

14. Our Website…

15. News and Conferences CAPTA Meeting (Oct, 2004) CSM Symposium, 2005

16. Training is a large part of all projects and involves students at many levels of prior training and professions. • Entry level DPT (MUSSEL, STEPS, PEDALS) • Masters level (STEPS, DMC) • Post-professional (STOMPS, PEDALS, STEPS, MUSSEL) • PhD (STEPS, MUSSEL, DMC) • Post-doctoral (STOMPS, PEDALS, STEPS, MUSSEL, DMC) …..and not just in physical therapy..

17. Companion studies have allowed more training opportunities.. • STEPS: • Companion ‘puller experiment’ Mark Rogers, PhD, PT, PI is mentoring Marie-Laure Mille, PhD (post-doctoral fellow) • Companion ‘gait study’ Sara Mulroy, PhD, PT, PI, and Kathy Sullivan, Co-PI are mentoring Tara Klassen (post-professional MS) • PEDALS: • Companion ‘CP gait study’, Fowler, PhD, PT and George Salem, PhD are mentoring Rich Souza (PhD student) and Kara Siebert (UCP clinical fellow)

18. The beauty of a flower: I have a friend who’s an artist and he’s sometimes taken a view which I don’t agree with very well. He’ll hold up a flower and say, “ Look how beautiful it is” and I’ll agree, I think. And he says—”you see, I as an artist can see how beautiful this is, but you as a scientist, oh, take this all apart and it becomes a dull thing.” And I think that he’s kind of nutty. First of all, the beauty that he sees is available to other people and to me, too, I believe, although I might not be quite as refined aesthetically as he is; but I can appreciate the beauty of a flower. At the same time I see much more about the flower than he sees. I can imagine the cells in there, the complicated actions inside which also have a beauty. I mean it’s not just beauty at this dimension of one centimeter, there is also beauty at a smaller dimension, the inner structure. Also the processes, the fact that the colors in the flower evolved in order to attract insects to pollinate it is interesting—it means that insects can see the color. It adds a question: Does this aesthetic sense also exist in the lower forms? Why is it aesthetic? All kinds of interesting questions which shows that a science knowledge only adds to the excitement and mystery and the awe of a flower. It only adds; I don’t understand how it subtracts. From: The Pleasure of Finding Things Out, the Best Short Works of Richard P. Feynman Do you have any questions?