“Effects of Running Speed on a Probabilistic Stress Fracture Model” W. Brent Edwards. Clinical Biomechanics. 2010.

1. Ellen Vanderburgh HSS 409 4/21/10 “Effects of Running Speed on a Probabilistic Stress Fracture Model”W. Brent Edwards. Clinical Biomechanics. 2010.

2. Stress Fractures: What are They? • Over-use injury • Cumulative mechanical trauma to bone or muscle • Muscle strain causes bone damage • Small crack within bone • Starts as microcrackand becomes macrocrack • “crack driving force” is greater than crack resistance • Cannot repair damage • In lower extremities-occur in load bearing bones • Metatarslas, femur, fibula and tibia • 15-20% overuse injuries tibial

3. Who is at Risk? • Athletes involved in repetitive, weight bearing, lower body activity • Ex: Runners • Low bone density • Bone cannot repair • Common in women • Female triad: abnormal eating, excessive exercising, amenorrhea • Poor footwear • Abrupt training increase

4. Predicting Tibial Stress Fracture Probability with Biomechanics • Crack driving force increases with loading magnitude (intensity) and crack length • Increases in speed • Increases in running cycles (aka strides) • High magnitude loading increases rate of microcracks- bone repair process cannot “catch up” • Crack resistance is less than crack driving force • Must identify loading patterns that cause bone strain • Loading magnitude, loading cycles, bone repair process, ground reaction forces, adaptation to activity

5. Purpose and Hypothesis of Study • Determine influence of running speed on the probability of tibial stress fracture during a new running regimen • Approximately 100 days • “Reducing running speed would decrease tibial strain enough to negate detrimental increased number of loading cycles associated with the reduction” • Prediction model!! • Use tibial strain measurement to predict relative risk for tibial fracture • Strain = Fracture risk

6. Subjects • 10 males • Mean age=24.9 • Mean mass=70.1 • All participated in running or athletic activity on weekly basis • Injury free • Prior to study, no physical activity for 3 months

7. Methods • Established joint center locations • Anthropometric measurements and retroreflective markers on anatomical landmarks • Static motion capture trial, while standing in anatomical position • For each joint, x axis was anterior to posterior, y axis in axial direction, z axis was medial to lateral

8. Methods • Subjects ran over-ground at 2.5, 3.5 and 4.5 m/s (5.6, 7.8 and 10.1 mph) • Speed measured using motion capture of the horizontal component of L5S1 anatomical marker • 10 trials performed for each speed • Researcher measured time for 3 strides • Used to find subjects average stride frequency and stride length for each speed

9. Data Processing • Measured and averaged stride frequency for each speed • 2.5=20.3 Hz, 3.5=26.6 Hz, 4.5=32.8 Hz • Took three dimensional joint and segment angles • Used flexion/extension, abduction/adduction, internal/external rotation sequence • Joint reaction forces and net internal joint moments were determined using inverse dynamics • Body segment masses, moments of inertia and center of gravity locations were also calculated

10. Data Processing: Musculoskeletal Modeling • Joint angles were interpolated to 101 points into a musculo skeletal model (SIMM model) and scaled to each subjects segment lengths http://www.musculographics.com/products/simm.html

11. Developing the Probalistic Model for Tibial Stress Fracture • Probability for Fracture= • Contact force – Reaction force • Contact force: • Ground reaction force due to loading intensity, speed and body weight • Reaction force: • Tibial strain damage, bone repair and bone adaptation

12. Probalistic Model for Stress Fracture: Tibial Contact Force • Used musculoskeletal data to determine contact force acting on tibia-cannot be directly calculated • Ankle joint contact force calculated as vector sum of reaction force and muscle forces crossing talocrural joint • Fibula absorbs 10% of ankle joint contact force • Therefore, contact force for tibia:

13. Probalistic Model of Stress Fracture: Bone Damage, Fatigue Life and Adaptation • Used probalistic model of bone damage, repair and adaptation • Due to scatter in the fatigue life of bone, probability of failure when there is scatter was calculated using • The cumulative probability for bone repair, taking into account for failure, repair and adaptation with respect to time was determined as

14. Results • Joint contact force acting on distal tibia increased with running speed • Axial component across longitudinal axis of tibia was the dominant force • Mean peak instantaneous tibial contact forces were used to determine the instant of peak resultant force

15. Results • The number of loading exposures decreased with a decrease in running speed due to positive relationship between speed and stride length • For 4.8 km/day, loading exposure (strides)for each speed: • 2.5 m/s=2435 • 3.5 m/s=1829 • 4.5 m/s=1549

16. Results • Probability of failure peaked and leveled off after 40 days of training (within the 100 day new training regimen) • Decrease in speed resulted in a decrease in probability for fracture • From 4.5-3.5 m/s=7% decrease • From 3.5-2.5 m/s=10% decrease

17. Discussion • Hypothesis of article was supported in that the probability for tibial stress fracture was decreased with a decrease in speed • This also supports the idea that a decrease in speed will negate the damage done by the increase in loading cycles with the decrease in speed • A decrease in run speed may reduce risk for tibial stress fracturing • Risk for fracturing plateaus after 40 days of new regimen • **Note: Does not consider biomechanical misalignments or abnormalities

18. Significance to HSS 409 • Complexity of dynamic muscle equations and forces • Dealt only with single joints in static, non-weight bearing positions • Need to incorporate numerous angles, centers of gravity, limb lengths to characterize dynamic movements • Also not just x and y, but also z (3D)

19. Significance to HSS 409 • BIO+MECHANICS • Physiological component + engineering component • Prediction modeling • In class- military scaling, back-pack equation • Development of derived constants • Based on anthropometric analysis, but needs to actually be tested

20. Practical Implications • Speed is big factor in recovery and bone adaption • Important to consider gradual period during beginning of training • First time race: marathon, etc. • Recovering from injury: basically starting over • Injury potential= very fine line • Military • Extremely intense training • High risk and incidence of stress fracture