“EFFECTS OF RUNNING SPEED ON A PROBABILISTIC STRESS FRACTURE MODEL”W. BRENT EDWARDS. CLINICAL BIOMECHANICS. 2010.

Ellen VanderburghHSS 4094/21/10

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 microcrack and 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

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

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

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

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

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

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

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

Data Processing: Musculoskeletal Modeling

Joint angles were interpolated to 101 points into a musculo skeletal model (SIMM model) and scaled to each subjects segment lengthshttp://www.musculographics.com/

products/simm.html

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

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:

43

31

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ic fRFankletibiaF

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

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1

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sfa

t

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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

Tibial Contact Force (BW)

Speed (m/s) Anterior-Posterior

Axial Medial-Lateral

2.5 -.53 10.73 .51

3.5 -.62 12.63 .61

4.5 -.66 13.80 .68

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

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% decreaseSpeed (m/s)

2.5 3.5 4.5

Probability for Failure

.09 .19 .26

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

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)

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

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