Upper Limb Rehabilitation Robotics in Sub-Acute Spinal Cord Injury

1. Upper Limb Rehabilitation Robotics in Sub-Acute Spinal Cord Injury José Zariffa, Ph.D. January 23rd, 2014

2. J. Zariffa 2014

3. Upper-Limb Rehabilitation Adapted from Whiteneck et al., 1999 Total assist Partial assist Independent J. Zariffa 2014

4. Upper-Limb Rehabilitation Anderson, 2004 J. Zariffa 2014

5. Robotic Rehabilitation www.hometelemed.com interactive-motion.com J. Zariffa 2014

6. The Armeo®Spring (Hocoma, AG) J. Zariffa 2014

7. Study Goals • Evaluate the applicability and effects of the Armeo®Spring in a sub-acute (in-patient) cervical SCI population. Can the device: • Deliver more rehabilitation with efficient use of therapist time? • Improve subject engagement in their rehabilitation? • Improve functional outcomes? • Provide clinically meaningful information about subjects’ function? J. Zariffa 2014

8. Methods • Multi-centre pilot study: • GF Strong Rehabilitation Centre (Vancouver) • Lyndhurst Rehabilitation Centre (Toronto) • 12 subjects completed training (motor level C4-C6, AIS A-D) – compare limbs with Armeo® training to limbs without. • 16.1 ± 4.6 sessions over 5.2 ± 1.4 weeks (Avg: 3.2 sessions/week). J. Zariffa 2014

9. Analysis Groups J. Zariffa 2014

10. Outcomes 1) GRASSP: Graded and Redefined Assessment of Strength, Sensibility and Prehension. 4) SCIM: Spinal Cord Independence Measure • 2) ARAT: Action Research Arm Test • 3) Grip Dynamometers J. Zariffa 2014

11. Study Goals • Deliver more rehabilitation with efficient use of therapist time? • Improve subject engagement in their rehabilitation? • Improve functional outcomes? • Provide clinically meaningful information about subjects’ function? J. Zariffa 2014

12. Feasibility Outcomes(Therapist time) 0.61 0.41 0.25 First 3 sessions Overall Last 3 sessions Active assist time Total session length J. Zariffa 2014

13. Study Goals • Deliver more rehabilitation with efficient use of therapist time? • Improve subject engagement in their rehabilitation? • Improve functional outcomes? • Provide clinically meaningful information about subjects’ function? J. Zariffa 2014

14. Feasibility Outcomes(Subject questionnaire) J. Zariffa 2014

15. Study Goals • Deliver more rehabilitation with efficient use of therapist time? • Improve subject engagement in their rehabilitation? • Improve functional outcomes? • Provide clinically meaningful information about subjects’ function? J. Zariffa 2014

16. GRASSP Results(All subjects) J. Zariffa 2014 Zariffa et al., Spinal Cord, 2012

17. GRASSP Results(Subjects with partial hand function) J. Zariffa 2014

18. GRASSP Results(Subjects with motor incomplete injuries) J. Zariffa 2014

19. ARAT Results J. Zariffa 2014

20. Dynamometer Results J. Zariffa 2014

21. Study Goals • Deliver more rehabilitation with efficient use of therapist time? • Improve subject engagement in their rehabilitation? • Improve functional outcomes? • Provide clinically meaningful information about subjects’ function? J. Zariffa 2014

22. Diagnostic Application of Robotic Rehab • Manual clinical assessments are time-consuming to perform and require well-trained personnel. This limits the frequency of assessment. • More frequent assessments would allow rehabilitation programs to be more responsive. • Can a robotic rehab device be used to predict clinical assessment scores? J. Zariffa 2014

23. Prediction of Clinical Scores • Armeo® Spring records kinematic and grip data during use. J. Zariffa 2014

24. Prediction of Clinical Scores Range of Motion Smoothness Grip Ability Manual Assessment Scores J. Zariffa 2014

25. Prediction of Clinical Scores J. Zariffa 2014 Zariffa et al., IEEE TNSRE 2012

26. Prediction of Clinical Scores J. Zariffa 2014

27. Prediction of Clinical Scores J. Zariffa 2014

28. Prediction of Clinical Scores • Predictors vary by outcome, but all predictive models combine range of motion and grip information. J. Zariffa 2014

29. Smoothness of Movement • Smoothness was least related to functional abilities. This contrasts with what has been found in the stroke literature. J. Zariffa 2014

30. Summary and Lessons 1) Feasibility: • Reduction in therapist time was confirmed. • Impact on subject motivation was moderate. 2) Preliminary investigation of functional benefits: • Larger studies are required. • Indication of sensory benefits. • Focus on individuals with partial hand function. 3) Diagnostic application of robotic rehabilitation: • Successful prediction of clinical scores from Armeo®Spring data: more responsive rehabilitation; save time and money on assessments. J. Zariffa 2014

31. Wider Context J. Zariffa 2014

32. Example: VA ROBOTICS Clinical Trial • Multicentre RCT, 127 patients with chronic stroke. • Groups: • Upper limb rehab robot (MIT-Manus) • Intensive comparison therapy • Usual care • Conclusions: “In patients with long-term upper-limb deficits after stroke, robot-assisted therapy did not significantly improve motor function at 12 weeks, as compared with usual care or intensive therapy. In secondary analyses, robot-assisted therapy improved outcomes over 36 weeks as compared with usual care but not with intensive therapy.” J. Zariffa 2014 AC Lo et al., N Eng J Med, 2010.

33. Economic Analysis of VA ROBOTICS “The average cost of delivering robot therapy and intensive comparison therapy was $5152 and $7382, respectively (P<0.001), and both were significantly more expensive than usual care alone (no additional intervention costs). At 36 weeks postrandomization, the total costs were comparable for the 3 groups ($17 831 for robot therapy, $19 746 for intensive comparison therapy, and $19 098 for usual care). Changes in quality of life were modest and not statistically different.” TH Wagner et al., Stroke, 2011. J. Zariffa 2014

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35. Acknowledgments Vancouver: J.L.K. Kramer P. Taylor R. Willms A. Townson J.D. Steeves • Toronto: N. Kapadia M. Alizadeh-Meghrazi V. Zivanovic M.R. Popovic • Zurich: U. Albisser A. Curt J. Zariffa 2014