“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
9.i
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
nT
T
tdt
teq
/1
0
1
w
fso
sfa
t
t
V
VP exp1
v
rt
texp1Pr
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
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