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Abstract Preventing bone stress injuries (BSI) requires a deep understanding of the condition’s underlying causes and risk factors. Subject-specific computer modeling studies of gait mechanics, including the effect of changes in running speed, stride length, and landing patterns on tibial stress injury formation can provide essential insights into BSI prevention. This study aimed to computationally examine the effect of different exercise protocols on tibial fatigue life in male and female runners during prolonged walking and running at three different speeds. To achieve these aims, we combined subject-specific magnetic resonance imaging (MRI), gait data, finite element analysis, and a fatigue life prediction algorithm, including repair and adaptation’s influence. The algorithm predicted a steep increase in the likelihood of developing a BSI within the first 40 days of activity. In five of the six subjects simulated, faster running speeds corresponded with higher tibial strains and higher probability of failure. Our simulations also showed that female subjects had a higher mean peak probability of failure in all four gait conditions than the male subjects studied. The approach used in this study could lay the groundwork for studies in larger populations and patient-specific clinical tools and decision support systems to reduce BSIs in athletes, military personnel, and other active individuals. 

Background:Athletes, especially female athletes, experience high rates of tibial bone stress injuries (BSIs). Knowledge of tibial loads during walking and running is needed to understand injury mechanisms and design safe running progression programs. Purpose:To examine tibial loads as a function of gait speed in male and female runners. Study Design:Controlled laboratory study. Methods:Kinematic and kinetic data were collected on 40 recreational runners (20 female, 20 male) during 4 instrumented gait speed conditions on a treadmill (walk, preferred run, slow run, fast run). Musculoskeletal modeling, using participant-specific magnetic resonance imaging and motion data, was used to estimate tibial stress. Peak tibial stress and stress-time impulse were analyzed using 2-factor multivariate analyses of variance (speed*sex) and post hoc comparisons (α = .05). Bone geometry and tibial forces and moments were examined. Results:Peak compression was influenced by speed ( P < .001); increasing speed generally increased tibial compression in both sexes. Women displayed greater increases in peak tension ( P = .001) and shear ( P < .001) than men when transitioning from walking to running. Further, women displayed greater peak tibial stress overall ( P < .001). Compressive and tensile stress-time impulse varied by speed ( P < .001) and sex ( P = .006); impulse was lower during running than walking and greater in women. A shear stress-time impulse interaction ( P < .001) indicated that women displayed greater impulse relative to men when changing from a walk to a run. Compared with men, women displayed smaller tibiae ( P < .001) and disproportionately lower tibial forces ( P≤ .001-.035). Conclusion:Peak tibial stress increased with gait speed, with a 2-fold increase in running relative to walking. Women displayed greater tibial stress than men and greater increases in stress when shifting from walking to running. Sex differences appear to be the result of smaller bone geometry in women and tibial forces that were not proportionately lower, given the womens’ smaller stature and lower mass relative to men. Clinical Relevance:These results may inform interventions to regulate running-related training loads and highlight a need to increase bone strength in women. Lower relative bone strength in women may contribute to a sex bias in tibial BSIs, and female runners may benefit from a slower progression when initiating a running program.