The race to prevent running injuries
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This document presents a webinar by Reed Ferber, PhD, focusing on injury prevention in runners, emphasizing the importance of biomechanics, strength, and proper training methods. Research findings indicate that a significant percentage of runners experience injuries annually, and strategies such as using multiple shoes and participating in cross-training can reduce risk. The session highlights advancements in gait analysis technology and the development of a big data approach for predicting running injuries.
The race to prevent running injuries
- 1. The Science Behind Running Injury Prevention Reed Ferber, PhD ATC Associate Professor, Faculties of Kinesiology and Nursing Director, Running Injury Clinic October 11, 2016
- 2. Welcome Webinar series by University of Calgary scholars Information presented is a summary of the scholars’ research Please submit questions throughout the duration of the webinar Keep the conversation live on Twitter during the webinar using #exploreUCalgary
- 3. Welcome Reed Ferber Associate professor in the Faculties of Kinesiology and Nursing at the University of Calgary Research projects include pain treatment in runners, the biomechanics of running injuries and optimal treatment protocols for knee osteoarthritis
- 4. Outline Practical advice What we’ve done What we’re about to do What’s coming in the very near future
- 5. RunRepeat, 2014 50% Injured Each Year
- 9. Injury Prevention Research 264 runners followed over 22-week training program 33% sustained injury Malisoux et al., SJMSS, 2015
- 10. Injury Prevention Research 116 runners ran 91% of mileage in same shoe • Average 1.3 pairs 148 runners ran 58% of mileage in same shoe • Average 3.6 pairs • 39% lower risk of injury
- 11. Injury Prevention Research Participation in other sports • 25% reduced risk Previous injury increased risk by 72% • Treatment vs root cause
- 12. Practical Advice To prevent injuries: • Multiple shoes • Cross training • Previous injuries Determine the root cause of your injury
- 13. What We’ve Done
- 14. 15 Research Partners 57 Clinic Partners
- 16. Kobsar et al., 2015 Predict Treatment Response 85.3% Classification Accuracy
- 17. New Tech!
- 18. TEC Venture Prize – 2nd place
- 19. Wearable Tech
- 25. 0.20 0.25 0.30 0.35 0.40 0.45 0.50 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 X1 X2 Typical pattern
- 26. 0.20 0.25 0.30 0.35 0.40 0.45 0.50 1.5 2.5 3.5 4.5 X1 X2 Typical pattern
- 27. 0.20 0.25 0.30 0.35 0.40 0.45 0.50 1.5 2.5 3.5 4.5 X1 X2 Typical pattern Knee Brace
- 28. 0.20 0.25 0.30 0.35 0.40 0.45 0.50 1.5 2.5 3.5 4.5 X1 X2 Typical pattern Dirt Surface
- 29. 0.20 0.25 0.30 0.35 0.40 0.45 0.50 1.5 2.5 3.5 4.5 X1 X2 Typical pattern Typical Run
- 31. Summary Big data approach to predict injury We’ve developed some of the most sophisticated gait analysis technology Awesome advances in the near future Big Brother will be watching you!
- 32. Thank You
- 34. Thank you Sign up for other UCalgary webinars, download our eBooks, and watch videos on the outcomes of our scholars’ research at ucalgary.ca/explore/collections
- 35. Other Webinar Topics For ideas on other UCalgary webinar topics, please email us at exploreucalgary@ucalgary.ca